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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Against the backdrop of China's implementation of the "dual carbon" target and carbon emissions trading policies, renewable energy generation technologies have matured and received support from related policies. Distributed power sources have played a crucial role in the power system, and aggregators have integrated a large number of distributed power sources with diverse characteristics, shielding the complex characteristics of the underlying distributed power sources from grid scheduling. This article introduces the revenue optimization of low-carbon integration to optimize the aggregator's scheduling model, designs distributed renewable energy generation units, and studies the solution strategy based on quantum genetic algorithms for large-scale optimization scheduling problems. The aggregator's optimization variables divide the entire optimization problem and consider low-carbon integration to achieve distributed management of green energy parks, providing a feasible theoretical framework for the further development of distributed power sources. It has important practical significance in energy conservation, emissions reduction, and ecological environmental protection. [ABSTRACT FROM AUTHOR]

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG's profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG's profit is fairly allocated among the microgrids using the Shapley value method. The proposed method's effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications. [ABSTRACT FROM AUTHOR]

Copyright of Economic Series of the Bulletin of the L.N. Gumilyov ENU is the property of L.N. Gumilyov Eurasian National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Instead of expanding power plant capacities, which is an extremely expensive investment option, demand response offers an economical solution to the challenges arising from the variability and intermittency of the renewable energy resources and demand variations, particularly during demand peak periods. This paper proposes a multi-objective optimization framework for the optimal power flow problem that integrates a stepwise demand response involving flexible and aggregated loads. The process includes short-term demand forecasting using long short-term memory (LSTM) networks in a smart distribution grid, followed by the optimal allocation of energy storage systems, and load aggregators. By determining the optimal solution point of the multi-objective problem analytically, significant system costs and peak demand can be reduced without compromising system stability. Through numerical studies for a sample study case, a reduction of 22% in system costs, 2% in total voltage variation, and 10% in peak demand is observed for a negligible impact on customers' convenience. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart radial distribution network improves the two-way communication between the grid operator and electric vehicles (EVs) car parking operator through an aggregator. By two-way communication, EV energy management can be proposed in smart radial distribution networks. The EVs energy management in smart radial distribution networks helps in energy saving and also improves the reliability and stability of the network. A smart radial distribution network is proposed here with an aggregator, power market, G-2-V, and V-2-G facility. A novel energy exchange approach based on the profit-sharing principle for EV charging/discharging patterns is presented for three types of EVs of different capacities with state of charge uncertainties for benefits of EV owners, energy saving for grid operator and, vehicle-to-grid car parking operators. For introducing a real-time EV charging/discharging management approach an IEEE-33 bus system of the Saudi Arabia United Kingdom rural location is selected. The traffic pattern of Saudi Arabia is used for implementing the proposed charging/discharging approach with the three most popular EVs used in the city. The energy exchange algorithm presented here is implemented and tested on MATLAB 2020a software. Here, environmental impacts are also analyzed by calculating carbon emission particles. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach to the price-optimized charging of electric vehicles (EVs) based on energy flexibility. Fleet operators determine the minimum and the maximum power demand to charge EVs at a specific time and share this information as so-called power corridors (PCs) with an energy aggregator. The energy aggregator collects the predicted PCs from the fleet operators located in the same market area and aggregates the PCs. The energy provider periodically sends energy prices from the market to the energy aggregator, which purchases energy when its price is opportune. The energy aggregator calculates and delivers charge plans for each fleet operator involved and thus can pass along the purchase prices. The incentive design must ensure that fleet operators are better off by disclosing their flexibility data to the aggregator. This study can contribute to a new data-driven energy market communication system by providing insights on how to leverage the energy flexibility that EVs can offer to the energy system. [ABSTRACT FROM AUTHOR]

In multi-agent systems, it is important to design a reward based on the contribution of each agent for efficient learning. In this paper, we propose a reward distribution method for a surveillance system based on our previously proposed multi-agent reinforcement learning method with an aggregator, in which a control specification is described by a linear temporal logic formula. In this method, the aggregator computes and distributes rewards according to the actions that agents take on the surveillance system. Finally, the effectiveness of the proposed method is presented through a numerical simulation of a surveillance problem addressing a specific type of linear temporal logic specification. [ABSTRACT FROM AUTHOR]

Web archives capture, retain, and present historical versions of web pages. Viewing web archives often amounts to a user visiting the Wayback Machine homepage, typing in a URL, then choosing a date and time significant of the capture. Other web archives also capture the web and use Memento as an interoperable point of querying their captures. Memento aggregators are web accessible software packages that allow clients to send requests for past web pages to a single endpoint source that then relays that request to a set of web archives. Though few deployed aggregator instances exist that exhibit this aggregation trait, they all, for the most part, align to a model of serving a request for a URI of an original resource (URI-R) to a client by first querying then aggregating the results of the responses from a collection of web archives. This single tier querying need not be the logical flow of an aggregator, so long as a user can still utilize the aggregator from a single URL. In this paper, we discuss theoretical aggregation models of web archives. We first describe the status quo as the conventional behavior exhibited by an aggregator. We then build on prior work to describe a multi-tiered, structured querying model that may be exhibited by an aggregator. We highlight some potential issues and high-level optimization to ensure efficient aggregation while also extending on the state-of-the-art of memento aggregation. Part of our contribution is the extension of an open-source, user-deployable Memento aggregator to exhibit the capability described in this paper. We also extend a browser extension that typically consults an aggregator to have the ability to aggregate itself rather than needing to consult an external service. A purely client-side, browser-based Memento aggregator is novel to this work. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As large amounts of distributed renewable energy generation (DREG) replace conventional generating units on the grid, the tension between the supply lack of flexible resources and the increased demand for flexible resources on the grid intensifies. To address this issue, this paper focuses on distributed renewable energy generation aggregation (DREGA) applications based on energy storage systems (ESS). Considering the interconnection and supply–demand coupling between generation and consumption in the grid, ESS is used to flexibly regulate the flow of electrical energy between producers and consumers in DREGA. A pricing methodology that takes into account aggregation differences and real-time changes based on supply and demand is developed. This method mobilizes resources within the aggregator to help the network operator balance supply and demand through price incentives. Electricity is shared directly between producers and consumers within the aggregation, in addition to indirectly shared through ESS. It improves the self-consumption of renewable energy and the self-supply rate of users from the perspective of aggregators. The validity of the proposed method is proven by case study results.

Instead of expanding power plant capacities, which is an extremely expensive investment option, demand response offers an economical solution to the challenges arising from the variability and intermittency of the renewable energy resources and demand variations, particularly during demand peak periods. This paper proposes a multi-objective optimization framework for the optimal power flow problem that integrates a stepwise demand response involving flexible and aggregated loads. The process includes short-term demand forecasting using long short-term memory (LSTM) networks in a smart distribution grid, followed by the optimal allocation of energy storage systems, and load aggregators. By determining the optimal solution point of the multi-objective problem analytically, significant system costs and peak demand can be reduced without compromising system stability. Through numerical studies for a sample study case, a reduction of 22% in system costs, 2% in total voltage variation, and 10% in peak demand is observed for a negligible impact on customers’ convenience.

The integration of Electric Vehicles (EVs) equipped with Vehicle-to-Grid (V2G) technology has been recognized as a promising solution to enhance the flexibility of modern power distribution systems. However, to make this a reality, there is a need for flexible, reliable, and highly scalable computing architectures that can coordinate the charging and discharging process of EV fleets while identifying the best trade-offs between the vehicle charging expectations and the grid requirements. This challenging problem has been formalized in this paper as a welfare maximization problem, by defining proper utility functions to quantify the marginal cost/benefits of the charging and discharging process. Then, a self-organizing solution scheme is proposed in the task of enabling the EVs fleet to solve the welfare maximization problem by only exchanging local non-sensitive information. The insight is to define a formal connection between the charging/discharging scheduling problem and the economic dispatch of thermal generators, hence exploiting all the vast literature on the theory of the marginal cost for the decentralized solution of the scheduling problem, which is one of the main contributions of this paper. Simulation results obtained on two case studies are presented and discussed in order to assess the effectiveness of the proposed technique in solving complex scheduling problems in realistic operation scenarios.

This paper presents a methodology for optimizing the short-term operation of electric vehicle (EV) charging and discharging while considering the potential curtailment of active power due to volt-var control (VVC) prioritizing reactive power output. The proposed approach involves exchanging information between the EV aggregator and the distribution system operator (DSO). This approach allows the EV aggregator to optimize EV charge/discharge schedules while considering voltage-related constraints in the distribution system (DS). Initially, the aggregator shares the optimized schedule with the DSO to estimate the anticipated active power reduction through power flow analysis. Subsequently, the aggregator revises the constraint on active power output to avoid its expected curtailment and performs a second optimization for EV charging and discharging operation. Numerical simulations conducted on a realistic DS model in Japan validate the effectiveness of the proposed method in enhancing profitability in the day-ahead market while ensuring the quality of DS voltage. The results demonstrate an increase in profit by shifting the time of EV charging and discharging based on shared information from the DSO.

The increase in EV (Electric Vehicle) charging demands significantly affects the power system; thus, developing an optimal operating system is necessary to improve the power system stability while considering the transportation network. However, in existing studies, charging scheduling is divided into a power system perspective focusing on grid stability and a transportation perspective focusing on the optimal operation of EVs. As EVs affect both systems, it is necessary to consider both grid operation and transportation-network operation simultaneously. In this study, an integrated operation algorithm for the transportation network and power grid has been developed, which determines the amount of demand response through the power-flow results at the upper level and resolves traffic congestion by setting the optimal route for EVs at the lower level. The Demand Response (DR) adjustment amount is determined based on MATSim vehicle data, rather than simply looking at the DR amount from the perspective of the power system. Furthermore, the concept of Autonomous Integrated EVs (AIEV) in the integrated system has been constructed, which improves the grid stability to cope with uncooperative EVs. The transportation system is configured using MATSim to run the integrated simulation, and the power system is configured using the IEEE-33 bus model. The integrated operating algorithm received high evaluation indicators for voltage stability, power loss, and average driving time. In addition, the effectiveness of the algorithm was demonstrated in various situations through the composition of multiple scenarios.

Increasing penetration of distributed energy resources in the distribution network (DN) is threatening safe operation of the DN, which necessitates setup of the ancillary service market in the DN. In the ancillary service market, distribution system operator (DSO) is responsible for safety of the DN by procuring available capacities of aggregators. Unlike existing studies, this paper proposes a novel market mechanism composed of two parts: choice rule and payment rule. The proposed choice rule simultaneously considers social welfare and fairness, encouraging risk-averse aggregators to participate in the ancillary service market. It is then formulated as a linear programming problem, and a distributed solution using the multi-cut Benders decomposition is presented. Moreover, successful implementation of the choice rule depends on each aggregator's truthful adoption of private parameters. Therefore, a payment rule is also designed, which is proved to possess two properties: incentive compatibility and individual rationality. Simulation results demonstrate effectiveness of the proposed choice rule on improving fairness and verify properties of the payment rule.

Within large and growing human communities where interactions occur, trust is a key factor to consider. Computational trust models have then been widely studied since the 2000s targeting items ratings (e.g. in e-commerce) or M2M (e.g. in IoT network). Among these models, EigenTrust is today one of the most popular and studied ones. It provides a global reputation calculation and is efficient in distributed networks, but not fully satisfactory for human interactions. On the opposite, the Bi-lattice model is well suited for human networks interactions such as solidarity networks and/or human services exchange networks but is limited to local trust results. In this paper, we propose a new aggregator that extends the Bi-lattice model to enable a global reputation calculation. This new aggregator discovers the trust links from the member whose score is to be evaluated to every other members he is connected to on the trust network. It then computes the global reputation of this member based on these trust links. Furthermore, it enables a lightweight approach, as it is able to compute a global reputation based only on a partial knowledge of the trust network. Throughout the paper, the proposed aggregator is presented, evaluated and compared to Eigentrust to show its effectiveness.

The market operation of the electricity distribution system has gained significant expansion due to the growing use of renewable and distributed sources. This paper presents strategies for optimizing load schedules by aggregators of local systems. Optimization of load schedules is to minimize the total costs of the supplier to reduce the cost of electricity and lower prices for the consumer. In local systems, which include diesel generators, the main component that most affects the price per 1 kW is the consumption of primary fuel of diesel generators. The objective function proposed in this study minimizes the supplier's primary fuel consumption by optimally redistributing power to each generator. The basic load schedule and application of schedule optimization strategies were studied: load reduction, load transfer, increase of minimum loads, flexible load change. When using the optimal algorithm, the costs are 5-6% relative to the uniform power distribution. Ref. 8, fig. 4, table.

Alterations in the proportion of conventional generation to that of renewable energy source could be a future challenge for electric energy networks. Conventional power plants are being replaced by wind and solar energy. Solar PV plants and wind turbines lower the system inertia constant, causing the grid frequency to shift. Renewable energy generation plants that are integrated into the energy system may cause problems with frequency stability if adequate frequency control techniques are not applied. In this study, a Load Frequency Control framework based on aggregators is developed to improve the frequency response of the power system. It was demonstrated that the aggregator is capable of forecasting available flexibility for the day ahead contributing to the frequency control process. The aggregator is contributing to the grid frequency stabilization considering the price signal for minimizing energy costs, with an effect on end user’s energy bills reduction. For assessing the aggregator’s flexibility availability, the aggregator calculates the contributed power after minimizing its energy costs. The results show that it is possible to use DR technology to stabilize the grid frequency. With the loss of generation of 50 MW, the aggregators contribute mainly for the morning event with a contribution of about 4 MW followed by the night event with the contribution of approximately 3.5 MW. The largest contribution was obtained between 06:00 and 8:00 for the examined area with three aggregators, and the smallest contribution was obtained between 22:00 and 24:00. By considering the whole power system, with the integrated renewable energy resources in different periods, the findings demonstrate that even though the frequency decreases, it quickly returns to the usual operating limit and stabilizes itself back to the normal operating limits of 50 Hz as a result of the aggregator participation in the process of grid frequency stabilization.

This study has been conducted aiming at improvement of a multi-agent model whose task is planning and energy management of a power distribution system based on electric vehicles and their aggregators. In this work, the wear of automobile batteries is considered as an inhibitor agent for electric vehicle owners which affects other agents. Therefore, the aggregator agent should consider the cost as encouragement for the owners of electric vehicles. The agents used in this paper are: 1) Technical agent distribution system operator 2) Distribution System Operator market agents 3) Electric vehicle aggregator agents. This paper proposes a strategy for the aggregation agent of electric vehicles in a competitive electricity market, taking into account market reservations. This model provides a way to reimburse vehicle owners for battery burnout over the consumption cycle and it helps to increase the desire of electric vehicles to charge and sell electricity to the market and increase the profits of vehicle agents and owners. [ABSTRACT FROM AUTHOR]

The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role in economic activity. While categorized as non-productive energy users, the retail industry operates numerous establishments, facing substantial energy costs that make energy management integral to its operations. Historically, smaller retail stores have lacked awareness of energy saving. However, by connecting these stores, even modest reductions in individual electricity consumption can yield significant overall energy savings. This study aims to investigate the feasibility of implementing the demand-side management (DSM) aggregator model in the retail industry. Through surveys on awareness of energy saving and the application of deep learning techniques to analyze the effectiveness of the Aggregator model, the results reveal that the mean squared prediction error (MSPE) of this research is below 2.05%. This indicates substantial accuracy and offers meaningful reference value for Energy Service Company (ESCO) providers. The findings contribute practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities. [ABSTRACT FROM AUTHOR]

With the integration of most distributed energy resources or loads managed by aggregators into the distribution systems, a deviation will be observed between the bids of the aggregators and the actual power output in the electricity market if the constraints of the distribution systems are not taken into consideration by the aggregators, which will bring deviation penalties. To this end, a bi-level bidding model is hereby proposed for aggregators considering the safety check of the distribution system. The upper level considers bidding to maximize the profit of each aggregator and introduces the safety check of the distribution system as the lower-level problem. To ensure the safe operation of the distribution system and reduce the power deviation of the bidding plan of the aggregator, the node voltage deviation, active power margin of transmission, and the bidding ratio deviation rate of each aggregator are taken as the optimization goals of the distribution system. Additionally, the data privacy of each aggregator is preserved by using the alternating direction method of multipliers to decompose a bidding problem of aggregators considering the safety check of the distribution network into subproblems solved separately and in parallel. Finally, the rationality and effectiveness of the proposed method are illustrated using a modified IEEE33-bus system. [ABSTRACT FROM AUTHOR]

Implementing successful aggregated charging strategies for electric vehicles to participate in the wholesale market requires an accurate battery model that can operate at scale while capturing critical battery dynamics. Existing models either lack precision or pose computational challenges for fleet-level coordination. To our knowledge, most of the literature widely adopts battery models that neglect critical battery polarization dynamics favoring scalability over accuracy, donated as constant power models (CPMs). Thus, this paper proposes a novel linear battery model (LBM) intended specifically for use in aggregated charging strategies. The LBM considers battery dynamics through a linear representation, addressing the limitations of existing models while maintaining scalability. The model dynamic behavior is evaluated for the four commonly used lithium-ion chemistries in EVs: lithium iron phosphate (LFP), nickel manganese cobalt (NMC), lithium manganese oxide (LMO), and nickel cobalt aluminum (NCA). The results showed that the LBM closely matches the high-fidelity Thevenin equivalent circuit model (Th-ECM) with substantially improved accuracy over the CPM, especially at higher charging rates. Finally, a case study was carried out for bidding in the wholesale energy market, which proves the ability of the model to scale. [ABSTRACT FROM AUTHOR]

The proliferation of Distributed Energy Resources (DERs) is paving the way for new energy-efficient services that aim to make end-users more active. According to the literature, these services will be managed by a central figure, aggregators. This paper proposes several business models to accommodate them in the electric power industry. Several potential alternatives are identified from the study of different design elements, such as the control strategy, architecture, and signals that need to be exchanged. These alternatives are then tested by analyzing pairwise relationships between all the actors involved (Aggregator-Prosumers, Aggregator-DSO, and Aggregator-TSO). Every business model is first explained individually, including how contracts would operate, in order to determine their advantages and drawbacks. Finally, a comparison of all the alternatives is put forward together with an overview of the main initiatives that have already been implemented.

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Abstract This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and flexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum offer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model different kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fluctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets, reserve regulations, and risk preferences.

The rapid expansion of energy storage integration has not provided sufficient time to strengthen and expand the transmission and distribution network. This issue can lead to PCC congestion in green multi-microgrid (MMG) systems. In these systems, microgrids operate independently and connect to the grid at a point of common coupling (PCC) without sharing operational data with neighboring microgrids. To address this issue, this paper proposes a bi-level optimization model designed to reschedule hydrogen storage systems. The first level allows each microgrid to optimize its energy transactions with the grid and communicates any unbalanced energy to the second level, where a hydrogen management system (HMS) is introduced. The HMS optimizes virtual hydrogen prices to address the PCC congestion and maximize the MMG’s profit. These virtual prices are then sent to the first level, allowing the microgrids to reschedule the hydrogen storage systems based on these virtual prices. Finally, the MMG’s profit is fairly allocated among the microgrids using the Shapley value method. The proposed method’s effectiveness is demonstrated using simulations, which show a six percent increase in MMG profit compared to scenarios that only share PCC capacity while maintaining the data privacy of all the involved microgrids.

This proceeding presents a new approach to determining energy indicators, such as specific fuel consumption and the energy efficiency ratio of a power generating plant. The essence of the new method is to determine the instantaneous power of the installation followed by data processing through integration. The use of this method made it possible to establish the dependence of specific energy indicators on the load level of the generating plant. The established distri-bution allows you to determine the optimal parameters of the experimental installation and consumption at different load levels. The highest specific fuel energy consumption is observed (1189–1297 g/(kW×h) and 962–1147 g/(kW×h)) at the lowest and heaviest load with different types of fuel. Experimental data confirmed that fuel without ethanol impu-rities allows generating a larger amount of electrical energy, which is advisable to use when greater needs arise. Bibl 8, fig. 2, tables 3.

As the share of distributed generation increases, so do the opportunities for aggregators to participate in the electricity market. In particular, aggregators participating in both the day-ahead and real-time markets contribute to improving the reliability of the power system. In addition, aggregators seeking additional revenue can benefit from providing reserves in a joint electricity market environment. However, aggregated resources with uncertainty are limited because of the uncertain nature of both reserve provision and the amount of reserves they can provide. Therefore, this study proposes a robust optimization model for an aggregator to formulate a strategy for participation in the day-ahead markets and deploys energy control in the real-time operation. The serving ratio reflects the availability of the aggregator's reserve participation. Both the deployed up/down power and renewable energy in the real-time operation are considered as uncertain parameters to reflect the uncertainty. In the case study, we analyze the profit-maximization strategy of an aggregator that owns renewable energy resources and energy-storage systems under the variation interval for uncertain parameters and the serving ratio. The bidding strategies vary by the variation interval and the serving ratio. [ABSTRACT FROM AUTHOR]

This paper addresses the electricity pricing problem with demand-side flexibility. The interaction between an aggregator and the prosumers within a coalition is modeled by a Stackelberg game and formulated as a mathematical bi-level program where the aggregator and the prosumer, respectively, play the role of upper and lower decision makers with conflicting goals. The aggregator establishes the pricing scheme by optimizing the supply strategy with the aim of maximizing the profit, prosumers react to the price signals by scheduling the flexible loads and managing the home energy system to minimize the electricity bill. The problem is solved by a heuristic approach which exploits the specific model structure. Some numerical experiments have been carried out on a real test case. The results provide the stakeholders with informative managerial insights underlining the prominent roles of aggregator and prosumers. [ABSTRACT FROM AUTHOR]

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and flexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum offer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model different kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fluctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets, reserve regulations, and risk preferences. [ABSTRACT FROM AUTHOR]

This paper focuses on the possible roles of aggregators in the European electricity markets and the challenges and opportunities they face in participating in different market segments. Demand response (DR) is becoming increasingly important with the growth of renewable energy, and aggregators can play a critical role in balancing supply and demand in real time. This paper provides an overview of prices in electricity markets in which DR aggregators can participate and provides recommendations for aggregators regarding which markets to focus on. However, the regulatory framework for aggregators is still evolving in Europe, creating challenges for them to navigate different market designs, regulatory frameworks, and pricing mechanisms. Through a combination of literature review and data analysis, this paper aims to provide insights for aggregators on how to maximize profits and minimize risks in the European electricity market. The article achieves this by conducting an extensive analysis of various markets, comparing their essential attributes relevant to the functioning of aggregators. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach to the price-optimized charging of electric vehicles (EVs) based on energy flexibility. Fleet operators determine the minimum and the maximum power demand to charge EVs at a specific time and share this information as so-called power corridors (PCs) with an energy aggregator. The energy aggregator collects the predicted PCs from the fleet operators located in the same market area and aggregates the PCs. The energy provider periodically sends energy prices from the market to the energy aggregator, which purchases energy when its price is opportune. The energy aggregator calculates and delivers charge plans for each fleet operator involved and thus can pass along the purchase prices. The incentive design must ensure that fleet operators are better off by disclosing their flexibility data to the aggregator. This study can contribute to a new data-driven energy market communication system by providing insights on how to leverage the energy flexibility that EVs can offer to the energy system.

An optimal daily scheme is presented to coordinate a large population of heterogeneous battery electric vehicles when charging in daytime work solar-powered parking lots and discharging into homes during evening peak-demand hours. First, we develop a grid-to-vehicle strategy to share the solar energy available in a parking lot between vehicles where the statistics of their arrival states of charge are dictated by an aggregator. Then, we develop a vehicle-to-grid strategy so that vehicle owners with a satisfactory level of energy in their batteries could help to decongest the grid when they return by providing backup power to their homes at an aggregate level per vehicle based on a duration proposed by an aggregator. Both strategies, with concepts from Mean Field Games, would be implemented to reduce the standard deviation in the states of charge of batteries at the end of charging/discharging vehicles while maintaining some fairness and decentralization criteria. Realistic numerical results, based on deterministic data while considering the physical constraints of vehicle batteries, show, first, in the case of charging in a parking lot, a strong to slight decrease in the standard deviation in the states of charge at the end, respectively, for the sunniest day, an average day, and the cloudiest day; then, in the case of discharging into the grid, over three days, we observe at the end the same strong decrease in the standard deviation in the states of charge.

FERC Order 2222 paves the way for aggregated distributed energy resources (DERs) participation in the wholesale electricity market. A particular DER assumed to be widely available in the future is the distributed prosumer (DP), also called virtual power plant (VPP), which may host PV generation, and stationary and mobile battery energy storage systems, in addition to the on- site passive load. DP aggregation and participation in the day-ahead market ancillary service products (ASPs) require managing uncertainties associated with load consumption and photovoltaic generation, electric vehicle (EV) scheduling, market-clearance prices, etc. Outages in the distribution grid may distort these energy-limited resources from their optimal operating point, potentially impacting their ability to deliver the committed ASPs in real-time. To address these challenges, first, we develop a machine learning algorithm to predict the risk of outages in distribution feeders. Next, we incorporate the distribution feeder State of Risk (SoR) predictions with the bidding model of the DP aggregator to provide an informed decision-making tool for optimal participation in the energy and ASP markets. The simulation results demonstrate the efficacy and scalability of the proposed model in improving the aggregator profitability and preventing penalties for the inability to deliver ASPs due to unexpected energy capacity limits of DP assets.

The rapid development of electric vehicles (EVs) has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection. This paper focuses on the optimization of EV charging, which cannot be ignored in the rapid development of EVs. The increase in the penetration of EVs will generate new electrical loads during the charging process, which will bring new challenges to local power systems. Moreover, the uncoordinated charging of EVs may increase the peak-to-valley difference in the load, aggravate harmonic distortions, and affect auxiliary services. To stabilize the operations of power grids, many studies have been carried out to optimize EV charging. This paper reviews these studies from two aspects: EV charging forecasting and coordinated EV charging strategies. Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models. At the end of this paper, recommendations are given to address the challenges of EV charging and associated charging strategies.

Recently, the penetration of electric vehicle (EV) has increased, and several motor companies have declared a paradigm shift to EVs. However, most existing studies focusing on EV charging scheduling in power system operations neglect the integrated operation of transportation and distribution systems. Because EVs affect both systems, it is necessary to consider the grid operation and the individual EV’s preferences (charging cost and time) simultaneously. In this study, an integrated power-transportation system structure and operation algorithms are developed to analyze the impact of EVs on the distribution system. In addition, this study proposes an optimal EV driving model that consists of two driving patterns: routine driving and long-distance driving. For routine driving patterns, the Markov chain model is implemented considering the mileage and conditions of individual EVs. Subsequently, long-distance driving patterns are developed to select the optimal route through reinforcement learning. In addition, the EV aggregator structure of EV charging stations can effectively predict local charge demands and perform charge control in real-time. An integrated power-transportation system is developed using the IEEE RBTS system and the integrated system architecture and algorithms are developed using MATLAB. It utilizes the ACOPPF function of MATPOWER to evaluate systematic stability and derive DLMP (Distributed Locational Marginal Price). This study conducts performance verification in various scenarios, confirming the effect of charging control of EV aggregators considering system stability, loss reduction, and cost reduction of the entire system.