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Smart grids are capable of two-way communication between individual user devices and the electricity provider, enabling providers to create a control-feedback loop using time-dependent pricing. By charging users more in peak and less in off-peak hours, the provider can induce users to shift their consumption to off-peak periods, thus relieving stre...
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... marginal costs are instances of the random variables; we assume that their actual values are exogenously determined for use in the provider's optimization problem. Figure 6 shows the piecewise-linear cost structure for base-, intermediate-and peak-load plants; c i0 denotes the slope of base-load electricity generation costs. We assume that any revenue gain from reselling surplus electricity is included in c i0 . ...
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... efficiency, as is consistent with the data in [25]) and the remaining hydroelectric plants. Finally, the peak plants are gas turbines, which are the most expensive to operate [26], [31]. The distribution of energy supply from different sources is shown Fig. 9. The slopes of the cost functions for base-, intermediate-and peak-load plants (refer to Fig. 6) are taken from the production estimates in [31]. The marginal costs of moving from intermediate-to peak- load and base-to intermediate-load plants are calculated to be $62.46/MWh and $18.54/MWh respectively. For simplicity, we assume that the electricity generator charges the distributor these prices; in practice, a premium would be ...
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... In the first category, the end-users are assumed to be price-taking. These end-users are responding to the utility's price and do not consider that their schedule impacts the per unit price [32,33]. The second category is price-anticipating end-users. ...
Price-based demand side management involves end-users responding to per-slot energy prices to optimize their energy consumption and reduce bills. The per-slot energy price constitutes distribution use of system charges which makeup 19%-24% of it. Existing work primarily focuses on the interaction between distribution network operators, larger storage/generation facilities, and prosumers overlooking the fairness of tariff structures used to recover distribution network operators' revenue. This oversight leads to unfair pricing for end-users, as it disregards factors such as the distance power must travel and the utilization of devices like transformers and transmission lines. This study employ the MW-Miles Distribution use of System Charges charging methodology to address this issue and designs a method to couple per-slot energy price to distribution use of system charges, considering distribution network operator as market players in a Stackelberg game framework. Two cases are devised to update distribution network operator-controlled parameters, with three perturbation strategies discussed for each case. The interaction between independent end-users is modeled using a noncooperative game framework, analyzing Nash equilibrium existence and algorithm convergence. An IEEE-33 bus system with residential end-users, home appliances, distributed energy storage, and dispatchable distributed generation is chosen for analysis. To account for end-user discomfort due to power-shiftable devices, a discomfort objective is included alongside energy bill savings. The results demonstrate a 7.67% increase in distribution network operators' revenue when actively participating in demand-side management program compared to its passive role as a utility.
... Due to pricing incentives, these consumers might opt to alter their power consumption patterns, leading to deviations from their originally planned power usage. Recent trends indicate that the readiness of users to shift their load is becoming a key consideration in demand-side energy management, as users exhibit varying levels of willingness to adjust their load [45]. For consumers, using shiftable appliances without regard for DR incentives may result in no perceived inconvenience, leaving their original load profiles unchanged. ...
The integration of distributed energy resources (DERs) and digital technologies has accelerated the transition to decentralized energy systems. Among these technologies, blockchain stands out for its ability to facilitate peer-to-peer (P2P) energy trading efficiently and securely. This paper explores the concept of P2P energy trading within community microgrid systems, leveraging blockchain-based smart contracts. The proposed system integrates an incentive-driven demand response program directly into the smart contract framework, offering real-time rewards for load-balancing contributions. By incorporating the microgrid’s Energy Management System (EMS) and transparently recording all transactions on the blockchain, the proposed platform provides detailed data and immediate reward distribution. At the core of our system lies the Supply to Demand Ratio (SDR), ensuring fair energy exchange within the community. Dynamic pricing, enabled by blockchain and Tether (USDT) cryptocurrency, adjusts to real-time market conditions, enhancing transparency and responsiveness in energy trading. This adaptive pricing model fosters a more equitable and efficient trading environment compared to static approaches. Moreover, this system is tailored for community microgrids, emphasizing a community-centric approach. Local prosumers serve as validators in the blockchain network, aligning energy management decisions with community needs and dynamics. This localized engagement promotes efficiency and participation, fostering resilient, sustainable, and user-centric energy landscapes. Through rigorous analysis, we demonstrate the system’s effectiveness in optimizing economic efficiency, reducing operational costs, and increasing compliance rates. By combining blockchain technology with community-focused design principles, the proposed platform represents a significant advancement towards self-sufficiency and resilience in local energy systems.
... This will guarantee a significant reduction in the electricity bill [18]. However, an RTP implementation requires continuous real-time exchange between the energy supplier and the consumers, which is unattractive from the customer's point of view [19]. In addition, the great flow of information exchanged between the energy supplier and EMCs and the lack of efficient smart meters besides scheme complexity can be real barriers for that type of systems. ...
Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.
... Similarly, the decentralized DRP developed in [25] uses non-cooperative game theory to design appropriate dynamic prices to control the grid load at peak hours. In [26], prices are derived by formulating the electricity provider's cost minimization problem, which considers consumers' devicespecific scheduling flexibility and the provider's cost of purchasing electricity. In addition, in [27], a dynamic price DRP is proposed and analyzed based on Stackelberg games and with the goal to reduce the demand peaks. ...
This work introduces a decentralized mechanism for the fair and efficient allocation of limited renewable energy sources (RESs) among consumers in an energy community. In the proposed non-cooperative game, the self-interested community members independently decide whether to compete or not for access to RESs during peak hours and shift their loads analogously. In the peak hours, a proportional allocation policy is used to allocate the limited RESs among them. The existence of a Nash equilibrium (NE) or dominant strategies in this non-cooperative game is shown, and closed-form expressions of the renewable energy demand and social cost are derived. Moreover, a decentralized algorithm for choosing consumers' strategies that lie on NE states is designed. The work shows that the risk attitude of the consumers can have a significant impact on the deviation of the induced social cost from the optimal. Besides, the proposed decentralized mechanism is shown to attain a much lower social cost than one using the naive equal sharing policy.
... However, the potential applications of NILM systems in residential and industrial settings go beyond the recommendations aimed at energy saving. Energy disaggregation can also be used in the management of Smart Grids to monitor the loads connected to a distributed network and predict consumption peaks [28][29][30][31][32][33][34][35]. This allows the electricity distribution body to propose discount offers or tariff programs based on consumer habits. ...
... These parameters are provided by NILM systems. In [28,29], the reduction in the cost of electricity is achieved by formulating the problem as a minimization problem, taking into account the scheduling flexibility of household appliances by consumers. The results show that the system can reduce the cost of energy for consumers in a meaningful way. ...
Load monitoring systems make it possible to obtain information on the status of the various loads powered by an electrical system. The term “electrical load” indicates any device or circuit that absorbs energy from the system to which it is connected, and which therefore influences electrical quantities such as power, voltage, and current. These monitoring systems, designed for applications related to energy efficiency, can also be used in other applications. This article analyzes in detail how the information derived from Non-Intrusive Load Monitoring (NILM) systems can be used in order to create Energy Management Systems (EMS), Demand Response (DR), anomaly detection, maintenance, and Ambient Assisted Living (AAL).
... Several DR infrastructures have already been realized and implemented [10,12,23], but they require a significant time ahead for scheduling and performing DR events. Our approach's novelty is developing a responsive DR system where DR schedules are created and executed immediately upon a request, e.g., when the production of RES suddenly drops. ...
With the ongoing integration of Renewable Energy Sources (RES), the complexity of power grids is increasing. Due to the fluctuating nature of RES, ensuring the reliability of power grids can be challenging. One possible approach for addressing these challenges is Demand Response (DR) which is described as matching the demand for electrical energy according to the changes and the availability of supply. However, implementing a DR system to monitor and control a broad set of electrical appliances in real-time introduces several new complications, including ensuring the reliability and financial feasibility of the system. In this work, we address these issues by designing and implementing a distributed real-time DR infrastructure for laptops, which estimates and controls the power consumption of a network of connected laptops in response to the fast, irregular changes of RES. Furthermore, since our approach is entirely software-based, we dramatically reduce the initial costs of the demand side participants. The result of our field experiments confirms that our system successfully schedules and executes rapid and effective DR events. However, the accuracy of the estimated power consumption of all participating laptops is relatively low, directly caused by our software-based approach.
... Flexibility of a grid is a property that allows it to better respond to adverse events facilitating aforementioned auto-correction or reconfiguration of system components. For example, appliance-specific scheduling as discussed in Joe-Wong et al. (2012) is such a measure. It is interesting to note that microgrid technology, which is considered as a building block of smart grid, provides both flexibility and adaptability (i.e., self-healing) through intentional islanding. ...
Modern Power System is under tremendous stress due to ever-increasing load demand. So modern Power Systems need to be shaped by Power and Energy management strategy for the betterment of operation. One of the key features of load forecasting is to ensure proper Energy management of Power Systems and to maintain the Power system reliability. In a comprehensive way Load forecasting in Power System stands for predicting the load to be nearly accurate, which is a basic necessary step for maintaining the efficiency of Gencos, Discoms, and other participants of the Electrical Energy market. This manuscript represents a solution methodology using an artificial neural network for load forecasting. The load data and all-weather data are taken from Charkhi Dadri region Delhi. First, the input data are ranked according to the Performance indexes based on Fuzzy logic and then ANN is used (LEVENBERG-MARQUARDT training method) to predict the load of the different slots of June 2013according to ranking. May and June month past six years data (2008-13) ( load data and all-weather data) are taken from the Charkhi Dadri region of Delhi as input parameters. Last but not least the effects of load forecasting uncertainties are studied from a power system reliability point of view. The effectiveness is also checked through its implementation under the MATLAB environment.
... Flexibility of a grid is a property that allows it to better respond to adverse events facilitating aforementioned auto-correction or reconfiguration of system components. For example, appliance-specific scheduling as discussed in Joe-Wong et al. (2012) is such a measure. It is interesting to note that microgrid technology, which is considered as a building block of smart grid, provides both flexibility and adaptability (i.e., self-healing) through intentional islanding. ...
The ability of the power system to withstand, respond, and recover from a catastrophic event is an important factor often used to define the resilience of a power system. Environmental threats and human threats, such as cyber security attacks, may trigger these types of incidents. Cost-benefit analysis (CBA) is a proven method to assess the economic feasibility of development interventions. The cost of doing any project can be compared by using CBA and by knowing their net benefit and efficiency. A flexible framework for cost-benefit analysis can assist in assessing and prioritizing investments to improve the resiliency of the energy system. This chapter deals with the economic approach for calculating the benefit and cost of any project. Cost-benefit analysis should be seen as a preferred choice by proving that benefit outweighs the cost and providing significance to the community. As benefit and cost are difficult to quantify with uncertainty, when comparing the project resilience option, a certain degree of ambiguity or sensitivity should be considered.
... To estimate the shifting functions, a form of given function with adjustable parameters is introduced [22]. The shifting function r is regarded to exponentially decrease in time and increase in incentive amount: ...
... First, the solution obtained by (29) approaches the solution to (24). Second, when initialized correctly, the solution of (29) can be feasible for original problem (22). These advantages will be elaborated in Section 4. ...
... To enable the proposed algorithm to generate feasible strategy for (22), the following properties are given: ...
Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users’ willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid–ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically.
... Other relevant issues concerning microgrids have arised with the introduction of free energy markets. Consumers became able to produce energy (thus being called prosumers) and, in parallel, contract types, market models and pricing schemes have evolved (Mitter et al., 2010;Joe-Wong et al., 2012;Morstyn et al., 2019;Aussel et al., 2020). In liberalized markets, large-scale generators, suppliers, industrial consumers and other financial intermediaries trade energy in wholesale markets, including day-ahead auctions, where agents submit their bids and offers for delivery of electricity for each hour of the following day, before market closing time. ...
... Although microgrid energy dispatch has been well studied in the literature, existing methods do not investigate the subscription to multiple and flexible electricity contracts, or even committing to future amounts of energy usage, according to forward markets. The same holds true for works focused on dynamic electricity pricing (Mitter et al., 2010;Joe-Wong et al., 2012). The closest work is the one by Duan & Zhang (2013), which, based on Stochastic Optimization, proposed a dynamic contract mechanism to smooth out fluctuations of microgrids' purchasing from the main grid, with time-specific commitments. ...
The advent of smart grids came with several technological developments including new electricity market rules and regulation mechanisms. Microgrids can trade energy with the main grid to either sell its production surplus (from renewable energy sources) or buy an additional amount to support local consumers’ demand, which includes flexible loads, such as smart appliances and electric vehicles. In this scenario, smart control devices are important elements, executing real-time energy scheduling according to fluctuations in production and consumption. As we might expect, the main grid’s power generation and supply becomes more unscheduled and risky as energy trading quantities oscillate over time. This work studies a flexible energy contract subscription framework, coupled with a real-time command strategy, suited for energy scheduling of microgrids with uncertainty in both production and consumption. Our main contributions are a Robust Optimization model under budgeted uncertainty for contract subscription and a set of heuristic control strategies for the real-time energy scheduling. The robust model is capable of providing solutions for multi-period-ahead trading of energy, while minimizing the worst-case cost. We run an extensive computational case study on a real microgrid instance to confirm the efficacy of our solution approach.
