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Real-time pricing based on convex hull method for smart grid with multiple generating units
... Keywords Real-time pricing, KKT condition, nonlinear complementary function, smooth approximation function, smooth Newton algorithm [3,[9][10][11][12][13][17][18][19] . [14] . , , d u [15,16] ...
Nowadays, grid-connected renewable energy resources have widespread applications in the electricity market. However, providing household consumers with photovoltaic (PV) systems requires bilateral interfaces to exchange energy and data. In addition, residential consumers' contribution requires guaranteed privacy and secured data exchange. Day-ahead dynamic pricing is one of the incentive-based demand response methods that has substantial effects on the integration of renewable energy resources with smart grids and social welfare. Different metering mechanisms of renewable energy resources such as feed-in tariffs, net metering, and net purchase and sale are important issues in power grid operation planning. In this paper, optimal condition decomposition method is used for day-ahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms: feed-in-tariffs, net metering, and net purchase and sale in conjunction with carbon emission taxes. According to the stochastic nature of consumers' load and PV system products, uncertainties are considered in a two-stage decision-making process. The results demonstrate that the net metering with the satisfaction average of 68% for consumers and 32% for the investigated electric company leads to 28% total load reduction. For the case of net purchase and sale mechanism, a satisfaction average of 15% for consumers and 85% for the electric company results in 11% total load reduction. In feed-in-tariff mechanism, in spite of increased social welfare, load reduction does not take place.
- Regardless of their nature of stochasticity and uncertain nature, wind and solar resources are the most abundant energy resources used in the development of microgrid systems. In microgrid systems and distribution networks, the uncertain nature of both solar and wind resources results in power quality and system stability issues. The randomization behavior of solar and wind energy resources is controlled through the precise development of a power prediction model. Fuzzy-based solar PV and wind prediction models may more efficiently manage this randomness and uncertain character. However, this method has several drawbacks, it has limited performance when the volumes of wind and solar resources historical data are huge in size and it has also many membership functions of the fuzzy input and output variables as well as multiple fuzzy rules available. The hybrid Fuzzy-PSO intelligent prediction approach improves the fuzzy system's limitations and hence increases the prediction model's performance. The Fuzzy-PSO hybrid forecast model is developed using MATLAB programming of the particle swarm optimization (PSO) algorithm with the help of the global optimization toolbox. In this paper, an error correction factor (ECF) is considered a new fuzzy input variable. It depends on the validation and forecasted data values of both wind and solar prediction models to improve the accuracy of the prediction model. The impact of ECF is observed in fuzzy, Fuzzy-PSO, and Fuzzy-GA wind and solar PV power forecasting models. The hybrid Fuzzy-PSO prediction model of wind and solar power generation has a high degree of accuracy compared to the Fuzzy and Fuzzy-GA forecasting models.
The rest of this paper is organized as: Section II is about the analysis of solar and wind resources row data. The Fuzzy-PSO prediction model problem formulation is covered in Section III. Section IV, is about the results and discussion of the study. Section V contains the conclusion. The references and abbreviations are presented at the end of the paper.
Energy storage system (ESS) plays a key role in peak load shaving to minimize power consumption of buildings in peak hours. This paper proposes a novel energy management unit (EMU) to define an optimal operation schedule of ESSs by employing metaheuristic and mathematical optimization approaches. The proposed EMU uses a thermal energy storage system (TESS) and a battery energy storage system (BESS) to store the energy in off-peak periods and discharge it in high load demands. We formulate the charging/discharging schedule of TESS and BESS as an optimization problem. Then, particle swarm optimization (PSO) is employed to obtain the optimal schedule due to its computational time efficiency. The mathematical approach is also applied to prove the convexity of the problem and the uniqueness of the solution. Due to the different characteristics of the building loads, this paper divides the total load into shiftable and fixed loads. Moreover, to model the building components and loads, grey-box modeling is adopted. Results show that employing a combination of TESS and BESS achieves peak load shaving while reducing 42.2% of the required BESS capacity compared with the case where the BESS is only used. In addition, the results indicate the effectiveness and robustness of the proposed algorithm.
In this paper, the relation between the shadow price and the Lagrange multiplier for nonsmooth optimization problem is explored. It is obtained that the Lagrange multipliers are upper bounds of the shadow price for a convex optimization problem and a class of Lipschtzian optimization problems. This result can be used in pricing mechanisms for nonsmooth situation. Several nonsmooth functions involved in this class of Lipschtzian optimizations are listed. Finally, an application to electricity pricing is discussed.
Convex hull pricing is a well-documented method for coping with the non-existence of uniform clearing prices in electricity markets with non-convex costs and constraints. We revisit primal and dual methods for computing convex hull prices, and discuss the positioning of existing approximation methods in this taxonomy. We propose a dual decomposition algorithm known as the Level Method and we adapt the basic algorithm to the specificities of convex hull pricing. We benchmark its performances against a column generation algorithm that has recently been proposed in the literature. We provide empirical evidence about the favorable performance of our algorithm on large test instances based on PJM and Central Europe.
In addition to procuring energy, consumers in electricity markets procure demand response (DR) services. Demand and supply of energy in the electricity market drives the demand for DR services. Through the net benefits test (NBT), economic procurement of DR is limited to an amount that ensures that consumers benefit with the procurement of DR services. However, the NBT neither a) recognizes the coexistence of the DR market with the energy market; nor b) optimizes social welfare in the DR market in concert with that of the energy market. This lack of accounting for DR market surplus results in economic inefficiency. To address this shortcoming, we advance past works by: a) proposing a real-time DR market where the DR demand curve is a function of opportunity in the energy market; and b) co-optimizing energy and DR markets such that the total social welfare derived from both markets is maximized simultaneously. We also present an optimal power flow formulation and process to implement our ideas in real-time electricity markets. The formulation is tested on a simple test case and a system based on actual Pennsylvania-New Jersey-Maryland (PJM) data. For the PJM case, total social welfare is increased by 1.41% to 3.05% over existing DR procurement strategies, resulting in $14.5M to $30.9M additional benefits per hour.
The rapidly increasing penetration of distributed energy resources (DERs) calls for a hierarchical framework where aggregating entities handle the energy management decisions of small DERs and represent these DERs upstream. These energy management decisions are typically envisaged to be made via market-based frameworks, aspiring the so-called Local Electricity Markets (LEMs). A rich literature of studies models such LEMs adopting various modeling assumptions and proposes various Market Mechanisms towards making dispatch and pricing decisions. In this paper, we make a systematic presentation of a LEM formulation, elaborating on the cornerstone attributes of the market model, i.e. the Market Scope, the Modeling Assumptions, the Market Objective, and the Market Mechanism. We discuss the different market model choices and their implications and then focus on the prevailing approaches of Market Mechanisms. Finally, we classify the relevant literature based on the market model that it adopts and the proposed Market Mechanism, visualize the results and also discuss patterns and trends.
The coordinated implementation of demand response technology and dynamic energy prices facilitates the interaction among multiple stakeholders in the smart integrated energy system. To achieve the optimal operation between different entities in the system, this paper proposes a multi-objective optimization model for smart integrated energy system considering demand responses and dynamic prices that reflects the preferences of multiple stakeholders. Based on the tightly coupled characteristics of a multi-energy system, a flexible two-dimensional demand response model with spatio-temporal coupling characteristics is established. By analyzing the characteristics of multi-entities joint pricing, the dynamic energy price formulated with the participation of both supplier and demander is optimized, and the dynamic price control strategy of different stakeholders under different benefit weights is obtained. A case study verifies the effectiveness of the proposed method. According to the interest preferences of different entities, different strategies and operating mechanisms can be derived, which is conducive to improving the economy and reliability of the operation of the smart integrated energy system and promoting the interaction between multiple entities.
In this paper, a two‐stage pricing framework is proposed for the electricity market which is consisted of a generation company (GC), multiple electric utility companies (EUC) and consumers. In the electricity wholesale market, the EUCs will choose an agent to negotiate the wholesale price with GC. An appropriate wholesale price plays an important role in the stable operation of the electricity wholesale market. However, it is challenging to find the optimal wholesale price. Therefore, the Raiffa‐Kalai‐Smorodinsky bargaining solution (RBS) is applied to realize the pricing equilibrium which is 0.3 $ / KWh . In the electricity retail market, this study designs a retail pricing strategy based on the potential game, which can optimize both social welfare and the benefit of the EUCs. Moreover, the impact of demand disturbance on the benefit of the EUCs and GC is studied in the electricity retail market. Then an iterative pricing algorithm is proposed for the two‐stage pricing model. The simulation results reveal that the demand disturbance has little effect on the benefit of the EUCs and GC, indicating the reliable/promising robustness of the algorithm.
Recent advancement of distributed renewable generation has motivated microgrids to trade energy directly with one another, as well as with the utility, in order to minimize their operational costs. Energy trading among microgrids, however, confronts challenges such as reaching a fair trading price, maximizing participants' profit, and satisfying power network constraints. In this paper, we formulate the direct energy trading among multiple microgrids as a generalized Nash bargaining (GNB) problem that involves the distribution network's operational constraints (e.g., power balance equations, voltage limits). We prove that solving the GNB problem maximizes the social welfare and also fairly distributes the revenue among the microgrids based on their market power. To address the nonconvexity of the GNB problem, we propose a two-phase approach. The first phase involves solving the optimal power flow problem in a distributed fashion using the alternative direction method of multipliers to determine the amount of energy trading. The second phase determines the market clearing price and mutual payments of the microgrids. Simulation results on an IEEE 33-bus system with four microgrids show that the proposed framework substantially reduces total network cost by 37.2%. Our results suggest direct trading need be enforced by regulators to maximize the social welfare.
Electricity market models require energy prices for balancing, spot and short-term forward transactions. For the simplest version of the core economic dispatch problem, the formulation produces a well-defined solution to the energy pricing problem in the usual form of the intersection of the supply marginal cost curve and the demand bids. In the more general economic unit commitment and dispatch models, there may be no analogous energy price vector that is consistent with and supports the quantities in the economic dispatch solution. Uplift or make-whole payments arise in this condition. Comparison of three alternative pricing models illustrates different ways to define and calculate uniform energy prices and the associated impacts on the energy uplift required to support the least cost unit commitment and dispatch.
Dynamic pricing (a.k.a. real-time pricing) is a method of invoking a response in demand pricing electricity at hourly (or more often) intervals. Several studies have proposed dynamic pricing models that maximize the sum of the welfares of consumers and suppliers under the condition that the supply and demand are equal. They assume that the cost functions of suppliers are convex. In practice, however, they are not convex because of the startup costs of generators. On the other hand, many studies have taken startup costs into consideration for unit commitment problems (UCPs) with a fixed demand. The Lagrange multiplier of the UCP, called convex hull pricing (CHP), minimizes the uplift payment that is disadvantageous to suppliers. However, CHP has not been used in the context of demand response. This paper presents a new dynamic pricing model based on CHP. We apply CHP approach invented for the UCP to a demand response market model, and theoretically show that the CHP is given by the Lagrange multiplier of a social welfare maximization problem whose objective function is represented as the sum of the customer's utility and supplier's profit. In addition, we solve the dual problem by using an iterative algorithm based on the subgradient method. Numerical simulations show that the prices determined by our algorithm give sufficiently small uplift payments in a realistic number of iterations.
The unit commitment problem (UCP) is one of the fundamental problems in power systems planning and operations that comprises two decisions: commitment and dispatching of conventional generating units. The objective is to minimize total operating costs -fuel and start-up costs- while satisfying several operational and technical constraints. The UCP is characterized as a highly constrained mixed-integer nonlinear NP-hard problem which makes it difficult to develop a rigorous optimization method for real-size systems. Hence we devise an efficient mixed-integer quadratic programming formulation as an exact method with brand-new linear representations for each of the three crucial constraint sets namely minimum uptime/downtime start-up and ramp-up/down constraints. Furthermore to be able to solve a large-scale UCP and to deal with its complexities we propose a Genetic Algorithm-based matheuristic approach that can provide optimal/near-optimal solutions quickly thanks to its unique binary-integer coding scheme and several problem-specific operators. During the genetic evolution commitment and dispatching schedules are determined by combining genetic operations and the Improved Lambda Iteration Method reinforced by the incorporation of average fuel cost optimization and ramp rate limits. The final dispatching schedule is then determined via a start-up adjustment procedure and an efficient quadratic programming model. The computational experiments show that both proposed exact approach and GA-based matheuristic can provide satisfactorily good schedules even for large-scale conventional power systems in quite a reasonable computation time.
The “dual carbon goal” and “one country network” indicate the greater challenge of balancing supply and demand in natural gas pipeline networks. This paper proposes an optimization method for supply and demand balance of large and complex natural gas pipeline network to respond the demand changes of downstream users by making the peak shaving and flow rate allocation scheme, considering carbon emission targets to improve economic and environmental benefits. This method considers the pipeline network elements including bidirectional pipeline, multiple pipeline structure, compressor station and gas storage so on, and couples the hydraulic characteristics to accurately describe the hydraulic situation of the pipeline under each allocation scheme. Case 1 shows that the economic efficiency is optimized by 15.58% and compensates for the overgrowth demand generated through the initial stage. Compared with TGNET, the average relative error of hydraulic was only 2.89%. Case 2 shows that the optimal flow rate allocation scheme can be further improved in the case of multiple pipelines. Case 3 for a large-scale pipeline network reduces 58% annual carbon emissions by adjusting the flow rate allocation scheme. This study can provide decision support for the allocation and operation of natural gas pipeline network.
The intermittent nature of renewable energy resources creates extra challenges for the operation and control of the electricity grid. Demand flexibility markets can help in dealing with these challenges by introducing incentives for customers to modify their demand. Market-based demand-side management (DSM) have garnered serious attention lately due to its promising capability of maintaining the balance between supply and demand, while also taking customers’ preferences into consideration. Many researchers have proposed using concepts from mechanism design theory in their approaches to market-based DSM. In this work, we provide a review of the advances in market-based DSM using mechanism design. We provide a categorisation of the reviewed literature and evaluate the strengths and weaknesses of each design criteria. We also study the utility function formulations used in the reviewed literature and provide a critique of the proposed indirect mechanisms. We show that despite the extensiveness of the literature on this subject, there remains concerns and challenges that should be addressed for the realistic implementation of such DSM approaches. These include privacy concerns, market efficiency, scalability, convergence speed, and modelling the intertemporal dependence of electricity consumption. We draw conclusions from our review and discuss possible future research directions.
The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as Coyote Optimization Algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.
Due to growing environmental and economic constraints, countries are exploring renewable sources such as wind, solar, and fuel cells to save energy, and develop the use of dispersed generations. Thus, the use of electric vehicles (EVs) is on the rise. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with suitable consumption-side management and programming, technologies and energy storage resources can reduce these effects. Thus, energy management optimization has become an interesting topic of research. Accordingly, the effect of the integrated aggregation of PEVs to the grid for the charge/discharge process and the resulting grid instability, especially at load peak time, is the main challenge to the use of these vehicles. The contribution of this paper includes the presentation of a model for managing the coordinated and uncoordinated charging system of grid-connected EVs with wind power and photovoltaic power units as dispersed generation sources and dividing the EVs into 4 classes by considering the share of each in the grid and considering a random number of vehicles per class using the normal distribution function and implementing the incoordination in wind speed and solar irradiation. The proposed model uses a novel Reinforcement Learning (RL) based onDeep Q Network (DQN) algorithm to solve the multi-objective problem. In this model, the costs of annual energy losses and the operation of dispersed generation units are discussed in an integrated manner as the objective function. The simulation is performed on a 57-bus IEEE grid and the results show the efficiency and improved performance of the model.
The energy internet (EI) integrated with smart grid (SG) has been a growing and emerging technology that manages and controls towards reliability, security, data integrity, demand response management, cyber-attacks, efficient utility energy service, and protocols. Nevertheless, EI-based SG implementation has several shortcomings, such as scalability, congestion, and pricing, making the entire system vulnerable and complex. Hence, this paper comprehensively reviews the EI concept for utility energy service and demand-side management (DSM) in SG, related issues, and future directions. In line with the matter, this review showcases an inclusive description of EI technology, highlighting architecture, theory, and applications. Besides, the various EI integrated utility services are discussed with regard to cloud-based utility service, one-stop online utility service, short message service-based utility service, future utility service, and affordable utility service. Moreover, the DSM in SG connected with EI environment is explored, covering the resilience of EI architecture, 5G based EI, and EI-based DSM for sustainable consumption. The numerous key issues, problems, and challenges are outlined to identify the existing research gaps. Finally, the review proposes some improvements for future opportunities and developments for EI in utility energy service and DSM in SG. All the critical discussion, analysis, and suggestions would be valuable for the power engineer and researchers to enhance EI-based SG for future sustainable operation and management.
The operational characteristics of generators cause non-convexity in the electricity market. The presence of non-convexity implies that market participants will bear lost opportunity costs under linear prices, which motivates them to violate the dispatch decisions or obtain excess profits through strategic bidding. To resolve this issue, finding a reasonable pricing scheme under non-convexity is a research field of great concern. Currently, most studies regard uplift payments as an indicator to quantify the effectiveness of a pricing scheme. It is usually recognized that convex hull pricing is a promising pricing method with nice incentive properties because it minimizes the uplift payment and thus improves market transparency. However, whether the pricing mechanism can guide truthful bidding has not been thoroughly investigated. In this paper, considering the complex market clearing process, we propose a framework with certain qualifications to analyze the incentive properties under different pricing schemes. Then, conclusions regarding the incentive property of different pricing schemes are provided. The effectiveness and correctness of the proposed method are validated using the numerical results of case studies.
A power system dominated by renewable energy is one of the key measures for achieving carbon neutrality. Demand response (DR) is a promising flexible resource for alleviating the supply-demand matching of high-proportion renewable energy systems. With the application of modern technologies, the potential for residential DR is growing. Electricity price is the key to improving residential DR capacity. However, existing dynamic pricing programs may fail to motivate end-users to adjust demand based on fluctuations in wind and photovoltaic (PV) output. This study proposes a dynamic pricing model that combines the fluctuation characteristics of residential electricity demand and wind and PV output, and utilizes bi-level optimization to coordinately dispatch the flexible loads. A case study of smart residential community consisting of 200 households shows that dynamic pricing incentivizes residential consumers to shift flexible loads from morning and evening to noon or early morning, which effectively improves the degree of matching between wind and PV output and residential electricity demand. Moreover, bi-level optimization effectively alleviates the potential rebound peak caused by large-scale residential participation in DR and achieves a relatively flat net grid demand profile. Furthermore, the dynamic pricing can incentivize residential consumers to participate in DR by reducing electricity bills.
This study considers a constrained huge-scale optimization problem over networks where the objective is to minimize the sum of nonsmooth local loss functions. To solve this problem, many optimization algorithms have been proposed by employing (sub)gradient descent methods to handle high-dimensional data, but the computation of the entire sub(gradient) becomes a computational bottleneck. To reduce the computational burden of each agent and preserve privacy of data in time-varying networks, we propose a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm over time-varying networks, in which the coordinates of the subgradient vector is randomly chosen to update the optimized parameter and the partially homomorphic cryptography is used to protect the privacy of data. Further, we prove that our algorithm is convergent asymptotically. Moreover, the rates of convergence are also established by choosing appropriate step sizes, i.e., O(logK/K) under local strong convexity and O(logK/K) under local convexity, in which K represents the number of iterations. Meanwhile, we show that the privacy of data can be protected by the proposed algorithm. The results of experiments demonstrate the computational benefit of our algorithm on two real-world datasets. The theoretical results are also verified by different experiments.
In this study, the game theory approach has been used to perform Time-Of-Use (TOU) pricing for renewable and conventional energy supply chains with government intervention to achieve sustainable development goals. Also, a Demand Response Program (DRP) based on TOU pricing has been implemented to improve the profits of power producers and the energy consumption pattern of end customers. Decision variables included the price of conventional and renewable energy during low- and high-load periods, tax rate, and subsidies. These variables are determined in three scenarios with the goals of maximizing government revenue, maximizing social welfare, minimizing environmental impacts, and under two-game structures of cooperative and Nash between the producers. The equilibrium solutions of each game for the three scenarios were obtained by backward induction. The results showed that decisions related to energy prices and tariffs play a major role in achieving the goals of sustainable development, profits of supply chain members, and success in meeting consumer demand. For all three scenarios, the government revenue function and the social welfare functions earn higher values in the Nash game than in the cooperative game, but the environmental impacts and the producers' profit function earn respectively lower and higher values in the cooperative game.
Today, power system planning requires simultaneous attention to supply and demand side management. It has gained remarkable attention due to the advent of new technological paradigms such as smart grid concepts and renewable energy resources. In this study, the effects of Demand Response (DR) as an emerging strategy of demand side management have been investigated on generation investment planning for achieving a technical, flexible, economical, and reliable solution for the supply side in the long-term. To this end, taking the advantage of system dynamics concepts and tools, the modeling of both principal DR types, i.e., price-based and incentive-based, is explored. In the first type, we formulate a novel market-based mutual relevance between market clearing price and willingness to pay of the price-based loads for accurately obtaining the electricity price as the primary signal for the investment process. The uncertain behavior of loads in these programs is expressed with a new concept called Certainty of Responsiveness (COR). A novel index, namely Loss of Load Duration Margin (LM), is proposed to incentivize the participants in incentive-based DR. Dispatching of these loads has been done using options contract. Numerical simulations are performed on a typical test supply system. Wind and solar photovoltaic units as the leading renewables in power systems are considered alongside the conventional thermal technologies. Different regulatory decisions about the adjustable parameters of DR programs are also analyzed. The results validate the effects of DR programs on total installed generation capacity, market prices, reliability indices, and outage costs in the long-term.
Currently, generating electricity relies primarily on fossil energy, which is not only limited in resources but also harmful to the environment as it emits carbon dioxide and other gases. For contributing to carbon peaking and carbon neutrality, building a resource-saving and environment-friendly society, clean energy should be used for generating electricity as much as possible. Additionally, the overproduction of electricity by suppliers and the instability of power grids, which are caused by the information asymmetry between customers and power suppliers, can be solved by the smart grid based on smart meters. Motivated by the two aspects, a smart power system combining a microgrid system based on clean energy generation with a supplier based on fossil energy generation is considered. For pursuing maximal social welfare under joint power supply, a real-time pricing model is established. This model, which is convex, does not explicitly contain the electricity price variable such that the optimal Lagrange multiplier of its Karush–Kuhn–Tucker condition, i.e., shadow price, is taken as the optimal electricity price. For solving the Karush–Kuhn–Tucker system, the smooth Levenberg–Marquardt algorithm is used to solve its approximate smooth system of equations. The simulation results demonstrate that the proposed pricing model, joint supply mechanism and the smooth Levenberg–Marquardt algorithm are reasonable, feasible and effective.
Demand response during the peak load period can not only enhance the security of power system operation under accelerated climate change, but also can reduce the unnecessary generation capacity and environmental externalities. However, the current capability of demand response lags far behind the expected goals in China, threatening the sustainable development of the electricity sector. The mechanism which allocates the national targets to different provinces is still lacked, making the DR implementation difficult to materialize in China. Moreover, stakeholders do not have a clear understanding of the magnitude of DR benefits, so they have insufficient motivations to participate. To bridge these gaps, five mechanisms are established to allocate national DR targets and three key elements are designed to implement DR. The major findings are that: (1) The duration of peak load time is very short in China, which represents for only 1.6% of the total hours within a year. (2) The annual total benefits from implementing demand response are 13 billion yuan, and the avoided capacity cost dominates in the potential benefits. (3) The average trigger point of starting demand response activities is 38 GW in China. (4) 19:00 pm is the most needed daily time for conducting demand response in most provinces.
With the applications of big data, the research of real-time pricing method for smart grid has become increasingly important. Based on the demand side management and the real-time pricing model, the social welfare maximization model of smart grid is considered. We transform it by Karush-Kuhn-Tucker condition, then the social welfare maximization model is transformed into a nonsmooth equation by Fischer-Burmeister function. Then, taking advantage of simple calculation and small storage, we propose a new smoothing conjugate gradient method to solve real-time pricing problem for smart grid based on the social welfare maximization. Under general conditions, the global convergence of the new proposed method is proved. Finally, the numerical simulation results show the effectiveness of the proposed method for solving the real-time pricing problems for smart grid based on the social welfare maximization.
Electricity Market is structured to fund reliable electricity supply, meet the need of consumers, ensure the affordability of end-users, and support national economic development. In recent years, to meet challenging emission target set by Government, power system in the UK has a rapid increase of integration with various-scale Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs), which pushes the electricity market reform to accommodate the changes, encourage renewable energy integration, adopt new technologies, stimulate consumers participation, and ensure the power system resilience. The paper reviews the history of UK electricity market evolution, driving factors of reform, and the trend of current electricity market reform. In history, the UK electricity wholesale market has experienced three significant reform stages, which are introducing the Electricity Pool of England & Wales (the Pool) in the 1980s, implementing the New Electricity Trading Arrangements (NETA) in the 2000s, and performing the Electricity Market Reform (EMR) in 2013. To address the new emerging challenges in decarbonising power generation, the paper explains and analyses on-going electricity market changes and the trend for future electricity market reform.
The presence of non-convexities in electricity markets has been an active research area for about two decades. The --- inevitable under current marginal cost pricing --- problem of guaranteeing that no market participant incurs losses in the day-ahead (DA) market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method --- i.e., yielding exact, not approximate, CH prices --- with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept.
Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource’s capabilities, they create challenges for market clearing processes. For example, system operators may be required to execute side payments to participants whose costs are not covered through energy sales as determined via traditional locational marginal pricing schemes. Convex hull pricing minimizes this and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. Computing convex hull prices involves solving either a large-scale linear program or the Lagrangian dual of the corresponding non-convex scheduling problem. Further, the former approach requires explicit descriptions of market participants’ convex hulls.
While linear programs for computing convex hull prices are large, their structure is naturally decomposable by generators. Here, we propose and empirically analyze a Benders decomposition approach to computing convex hull prices that leverages recent advances in convex hull formulations for thermal generating units. We demonstrate across a large set of test instances that our decomposition approach only requires modest computational effort, obtaining solutions at least an order of magnitude faster than the equivalent large-scale linear programming approach. Overall, we provide a computationally feasible method for computing convex hull prices for industrial scale market clearing problems, enabling the possibility of practical adoption of this advanced pricing mechanism.
This paper considers the privacy-preserving optimal energy management problem for smart grids, which integrates both the power allocation of distributed energy resources on supply side and the demand response of distributed load demands on demand side. We first propose a cloud-edge computing structure of the smart gird and model the optimal energy management problem as the maximization problem of social welfare including the supply-side net benefit and the demand-side net utility while maintaining the supply demand balance and satisfying the operating constraints. A privacy-preserving average consensus algorithm is then developed, where each node sends the projected states to their neighbors to protect the privacy of the initial state. By applying the privacy-preserving average consensus algorithm, we propose a distributed privacy-preserving energy management problem algorithm based on the generalized alternating direction method of multipliers algorithm. A simulation example evaluated using the IEEE 162-bus benchmark system is provided to validate the effectiveness of the proposed algorithm finally.
With the penetration of intermittent renewable energy sources, greater uncertainty has been brought to the power generation system, creating increased challenges to real-time pricing (RTP). Different from the existing studies, this paper aims to design an RTP strategy for the smart grid which integrates multi-energy generation on the supply side. Without loss of generality, small-scale distributed energy generation and power storage devices for users are also considered. Taking the interests of both supply and demand sides into consideration, a bilevel stochastic model for real-time demand response in the framework of Markov decision process (MDP) is formulated. The model well captures the interactive characters of both sides. Regarding the difficulty of collecting exact information from users in a centralized way in practice, a novel distributed online multi-agent reinforcement learning algorithm is proposed to solve the MDP model without acquisition of the transition probabilities. Through the information interaction between the upper and lower levels, the real-time electricity prices are decided adaptively, meanwhile, the optimal strategy of power supply and consumption is obtained. Simulation results demonstrate that the proposed pricing method and algorithm have a good performance in cutting peak and filling the valley and guarantee the benefits of both supply and demand.
Setting different electricity prices for different types of loads can effectively reduce the peak power consumption in microgrids (MGs). This paper proposes a category-specific pricing strategy for demand response program in dynamic MGs that can efficiently utilize renewable energy to achieve peak shaving and valley filling via establishing a Stackelberg game model. A state characteristic clustering (SCC) based non-intrusive load monitoring (NILM) scheme is first proposed, by which both the MG market operator (MMO) and users can access the detailed power consumptions of shiftable and non-shiftable loads. MMO then specifies detailed electricity prices dynamically based on user-side demand and satisfaction feedback, while users adjust their shiftable loads in a timely manner accordingly. Through solving the game optimization problem, the uniqueness and existence of the Stackelberg equilibrium is derived. Moreover, a distributed solution algorithm is presented to seek the unique equilibrium. Finally, a real residential power dataset is used to verify the effectiveness of the proposed category-specific pricing strategy. Numerical results show that the strategy reduces the peak-valley difference significantly, mitigates the power imbalance, and improves the utility of MG participators.
Dynamic pricing strategy will play an increasingly important role in solving the contradiction between power supply and demand under the current electricity market environment. The performance of different users varies from the same pricing strategy, which further to exacerbate the difficulty of the optimal power dispatching. Therefore, a non-cooperative Stackelberg model based game theory is developed in this paper, which considers both the impact on load fluctuations in power grid and users’ dissatisfaction with electricity consumption. Firstly, users are divided into different types by induction and classification in this model to realize the comprehensive consideration of different types of electricity users. Secondly, two utility functions are set up in this model. The utility function of power supply side is set to represent the benefit obtained in the process of power supply by the power company. The utility function of the power demand side is set to express the dissatisfaction degree of electricity users. The developed model is solved by using the NSGA-Ⅱ algorithm in this paper. Finally, the developed model is applied to the actual case and the sensitivity analysis of relevant parameters is carried out to verify its effectiveness.
With the penetration of multiple distributed energy sources, demand side management (DSM) of the regional integrated energy system (RIES) becomes more complicated in the energy market. Real-time pricing (RTP) is an effective method for DSM, which can flexibly guide the supply and demand sides to adjust their behavior to participate in demand response (DR). In this paper, a hierarchical energy system is studied including multiple RIESs with multiple energy dispatch and supplement. To maximize the social welfare, a bilevel programming model is developed, in which the upper level aims at maximizing the profits of the supplier, and the lower level aims at maximizing the RIESs' welfare. Then, the proposed bilevel model is transformed into a mixed integer quadratic programming model using duality theory and Karush-Kuhn-Tucker conditions. Furthermore, the RTP strategy is obtained, and the optimal energy scheme of RIES is given in the solution. Compared simulations in different scenarios, the total social welfare is increased by about 14.12%, the peak-to-valley difference of power load and carbon emissions are reduced by 16.99% and 5.7% respectively after DR. The results show that the proposed bilevel model under the RTP is conducive to social economy and environment.
Electricity price mechanism is an important means to implement demand response, especially in the home energy management system. A reasonable pricing mechanism therefore can stimulate the enthusiasm of residential users and as well balance power supply and demand effectively. From the perspective of residential users, this paper establishes a residential user evaluation system based on an evaluation model by selecting indicators related to user characteristics and electricity consumption data, and as well proposes a new interactive real-time pricing mechanism. A constrained multi-objective optimization model is then constructed, and the optimal operation scheme of each appliance is optimized. Numerical simulation and case studies show that, the optimization model can accurately schedule operations of household appliances. Besides, under the action of interactive real-time pricing, the electricity load fluctuation rate and electricity cost are significantly reduced compared with other comparative cases. The results confirm that, interactive real-time pricing aside reducing the cost of energy for users, can also play an active role in reducing peak loads and increase off-peak load, thereby stabilizing the load fluctuation.
The utility function is very significant for solving the real-time pricing problem of smart grid. Based on the Logistic function, a new utility function is constructed to satisfy four properties of the utility function. In addition, from the perspective of social welfare, the real-time pricing optimization model of smart grid is established. By using the KKT conditions and the improved Fischer-Burmerister smoothing function, the optimization model is transformed into a smoothing equations problem and the smoothing Newton algorithm is used to obtain the optimal solution of the problem. The nonsingularity of the Jacobian matrix and the global convergence of the algorithm are proved. The simulation results show that, compared with previous quadratic and logarithmic utility functions, the new utility function can not only reduce the user’s electricity consumption and the supplier’s cost can but also improve the user’s utility and the total social welfare, which also indicates that the new utility function is effective in establishing the real-time pricing model of smart grid. Furthermore, the iteration times of several algorithms to solve the real-time pricing problem of smart grid are compared, which showed that the convergence rate of the smoothing Newton algorithm is very fast.
Convex hull pricing has been introduced recently to increase transparency and reduce uplift payments for U.S. wholesale markets. A convex primal formulation approach is one of the most efficient methods to obtain a high-quality convex hull price. Even though significant progress has been made, the co-optimization of energy and ancillary services is much more challenging due to the discrete nature in formulating ancillary services, for example, the regulating reserve commitment variables, which are binary introduced by Independent System Operators (ISOs) to capture the special characteristics of certain generators, such as combined-cycle units. In this paper, we propose convex primal formulations for convex hull pricing considering regulating, spinning, and online/offline-supplemental reserves, in which the regulating reserve commitment variables are considered. Accordingly, we can solve a linear program, instead of a mixed-integer one, which is much harder to solve, to derive the minimum uplift payments and exact convex hull price for the case without general ramping constraints. For the case with general ramping constraints, an upper bound of the minimum uplift payment can also be derived. The final computational experiments on Midcontinent ISO (MISO) cases verify the improvement in solution quality and computational cost by utilizing our proposed approach.
The unit commitment problem is one of the most significant and basic issues in the monitor, control, and operation of modern power systems, which has always been a subject of great concern to researchers and operators as the most extensive human-made system. Before restructuring, one of the main objectives of unit commitment problem was the minimization of the total operation cost of power plants subject to various constraints, including unit and network ones. As the privatization and restructuring process became more serious, the primary purpose of the unit commitment problem has been changed to maximizing the total profit, which led to the emergence of a new concept known as profit-based unit commitment problem. Accordingly, the maximization of the profit for generation companies, all over the studied period, is a top-priority direction. This paper presents a comprehensive overview of the profit-based unit commitment problem in restructured power systems by investigating the most important studies on this topic and providing a complete classification. It also outlines the challenges facing researchers in this field, offers new insights, and suggests future directions.
As distributed energy (DE) and storage devices being integrated into microgrids (MGs), demand side management (DSM) is getting more and more complicated. The real-time pricing (RTP) mechanism based on demand response (DR) is an ideal method for DSM, which can achieve supply–demand balance and maximize social welfare in the future. This paper proposes a hierarchical market framework to address RTP between the power supplier and multi-microgrids (MMGs). Firstly, an expectation bilevel model is proposed to adjust the energy scheduling of MMGs, including uncertain loads, multi-energy-supply and storage devices,etc. In the proposed bilevel model, the upper level aims to maximize the profit of the power supplier, while the lower level is formulated to maximize the expectation of total welfares for MMGs. Then, the lower level is transformed into a deterministic optimization problem by mathematical techniques. To solve the model, a hybrid algorithm-called distributed PSO-BBA, is put forward by combining the particle swarm optimization (PSO) and the branch and bound algorithm (BBA). In this algorithm, the PSO and BBA are employed to address the subproblems of upper and lower levels, respectively. Finally, simulations on several situations show that the proposed distributed RTP method is applicable and effective under uncertainties, and can reduce the computational complexity as well. The results show that the hierarchical energy dispatch framework is not only more reasonable but also can increase the profits of power suppliers and the welfare of MGs effectively.
A local energy trading in microgrids is one of the emerging concepts in the area of distribution networks. A proper business model is required to manage local energy trading. The pricing mechanism is crucial because the agreed energy price determines the benefits of local energy trading. Designing a proper pricing mechanism with a specific objective considering the privacy of agents and respecting physical network constraints is a challenging task. This paper proposes a decentralized algorithm for local energy trading in microgrids with an integrated pricing mechanism considering welfare maximization and network voltage management through local information exchange among neighbors. The proposed algorithm guarantees that the energy transactions do not violate voltage constraints in a physical network and agents' privacy is preserved. A two-stage approach is proposed to achieve fast convergence and increase the practicability of the algorithm. The simulation results are presented to verify the effectiveness of the proposed approach.
In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.