Abstract:With the implementation of the integrated electricity and gas market (IEGM), the smart energy hubs (SEHs) tend to participate in the market clearing for the optimization of the energy purchase portfolio. Meanwhile, the renewable energy is mushrooming at different scales of energy systems, which can introduce utility-level and distribution-level uncertainties to the operation of the IEGM and SEHs, respectively. Considering the impacts of divergent uncertainties, there exist complicated interactions between the IEGM clearing and the robust bidding of SEHs. The lack of consideration of such interactions may lead to inaccurate modeling of the IEGM clearing and cause potential market inefficiency. To handle this, a bi-level robust clearing framework of the IEGM considering the robust bidding of SEHs is proposed, which simultaneously considers the impacts of utility-level and distribution-level uncertainties. The proposed framework is partitioned into two levels. The upper level is the robust clearing mechanism of the IEGM. At this level, the uncertainty locational marginal electricity and gas prices are derived considering the utility-level uncertainties and the uncertainty-based bidding of SEHs. Given the price signals deduced in the upper level, the lower-level robust bidding of the SEH seeks the optimal bidding strategies while hedging against distribution-level uncertainties. To address the proposed framework, an effective algorithm combining column-and-constraint generation (C&CG) algorithm with the best-response decomposition (BRD) algorithm is formulated. The devised algorithm can efficiently solve the individual robust optimization model and coordinate the interaction of two levels. Numerical experiments are carried out to verify the effectiveness of the proposed framework. Moreover, the impacts of uncertainties on the market clearing results along with the optimal biddings of SEHs are further demonstrated within the proposed framework.

Abstract:The hybrid photovoltaic (PV)-battery energy storage system (BESS) plant (HPP) can gain revenue by performing energy arbitrage in low-carbon power systems. However, multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market. This paper proposes two coherent models to address these challenges. Firstly, a knowledge-driven penalty-based bidding (PBB) model for HPP is established, considering forecast errors of PV generation, market prices, and under-generation penalties. Secondly, a data-driven dynamic error quantification (DEQ) model is used to capture the variational pattern of the distribution of forecast errors. The role of the DEQ model is to guide the knowledge-driven bidding model. Notably, the DEQ model aims at the statistical optimum, but the knowledge-driven PBB model aims at the operational optimum. These two models have independent optimizations based on misaligned objectives. To address this, the knowledge-data-complementary learning (KDCL) framework is proposed to align data-driven performance with knowledge-driven objectives, thereby enhancing the overall performance of the bidding strategy. A tailored algorithm is proposed to solve the bidding strategy. The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory (NREL) and the New York Independent System Operator (NYISO).

Abstract:Energy equity refers to the condition in which access to the cleaner energy required by individuals is equally available to all. To relieve the energy expenditures-the key component in the concept of energy equity–of low-income communities, governments worldwide have imposed caps on soaring energy prices. However, the inherent mechanisms within the operational schedule remain undiscussed. This paper innovatively provides guidelines for operators to embed energy burden policies into the bulk power system model, by answering two critical questions. ①What is the impact on system price pattern when embedding the locational price constraints? ② How to reformulate the tie-line schedule to meet the equity thresholds? Consequently, a novel bi-level energy equity-constrained tie-line scheduling model is proposed. The conventional economic dispatch is solved at the upper level, and then a preliminary operational schedule is given to the lower level, where we propose an energy equity slackness component variable to evaluate the gap between preliminary and desired equity-satisfied operational schedules. The implicit constraints on the price are converted into explicit feasibility cuts with dual theory. Case studies on test systems demonstrate the reduced energy expenditure for underserved communities, and the optimal tie-line schedule is also validated.

Abstract:To address environmental concerns, there has been a rapid global surge in integrating renewable energy sources into power grids. However, this transition poses challenges to grid stability. A prominent solution to this challenge is the adoption of battery energy storage systems (BESSs). Many countries are actively increasing BESS deployment and developing new BESS technologies. Nevertheless, a crucial initial step is conducting a comprehensive analysis of BESS capabilities and subsequently formulating policies. We analyze the current roles of BESS and review existing BESS policies worldwide, which focuses on key markets in Asia, Europe, and the U.S.. Using collected survey data, we propose a comprehensive three-phase framework for policy formulation, providing insights into future policy development directions.

Abstract:With the development of the carbon markets (CMs) and electricity markets (EMs), discrepancies in prices between the two markets and between two time periods offer profit opportunities for generation companies (GenCos). Motivated by the carbon option and Black-Scholes (B-S) model, GenCos are given the right but not the obligation to trade carbon emission allowances (CEAs) and use instruments to hedge against price risks. To model the strategic behaviors of GenCos that capitalize on these cross-market and cross-time opportunities, a multi-market trading strategy that incorporates option-jointed daily trading and reinforcement learning-jointed weekly continuous trading are modeled. The daily trading is built with a bi-level structure, where a profit-oriented bidding model that jointly considers both the optimal CEA holding shares and the best bidding curves is developed at the upper level. At the lower level, in addition to market clearing models of the day-ahead EM and auction-based CM, a B-S model that considers carbon trading asynchronism and option pricing is constructed. Then, by expanding the daily trading, the weekly continuous trading is modeled and solved using reinforcement learning. Binary expansion and strike-to-spot price ratio are utilized to address the nonlinearity. Finally, case studies on an IEEE 30-bus system are conducted to validate the effectiveness of the proposed trading strategy. Results show that the proposed trading strategy can increase GenCo profits by influencing market prices and leveraging carbon options.

Abstract:The spot flexibility markets are before the real-time energy exchange, allowing demand-side management to reduce energy consumption during peak periods. In these markets, demand aggregators must quickly choose the customers ’reduction bids that fulfill grid requirements. This clearing procedure is challenging due to the computational complexity of selecting the optimal bids. Therefore, developing a clearing mechanism that avoids searching the entire flexibility bid space while respecting grid constraints is essential for the smooth operation of the spot flexibility market. This paper presents a clearing mechanism with reduced computational complexity of the winner determination problem in spot flexibility market for demand aggregators carrying out reductions in energy consumption. The proposed approach transforms customers’flexibility bids into a reward-based function. Afterward, the gradient-based optimization solves the bid selection problem. This approach helps demand aggregators achieve satisfactory energy reductions within an appropriate delay for spot flexibility markets. A comparative study presents the effectiveness of the proposed approach against commonly used approaches: hybrid particle swarm optimization genetic algorithm and combinatorial search.

Abstract:Peer-to-peer (P2P) energy trading provides a promising solution for integrating distributed microgrids (MGs). However, most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator (DSO). Therefore, a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO. The interaction between DSO and MG is formulated as a Stackelberg game, in which the existence and uniqueness of optimal dynamic network usage fees are proven. Additionally, the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions. Furthermore, the underlying relationship among optimal dynamic network usage fees, electrical distance, and power flow is revealed, and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs. Finally, simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08% reduction in operation costs for MG while increasing DSO revenue by 15.36%.

Abstract:The virtual power plant (VPP) facilitates the coordinated optimization of diverse forms of electrical energy through the aggregation and control of distributed energy resources (DERs), offering as a potential resource for frequency regulation to enhance the power system flexibility. To fully exploit the flexibility of DER and enhance the revenue of VPP, this paper proposes a multi-temporal optimization strategy of VPP in the energy-frequency regulation (EFR) market under the uncertainties of wind power (WP), photovoltaic (PV), and market price. Firstly, all schedulable electric vehicles (EVs) are aggregated into an electric vehicle cluster (EVC), and the schedulable domain evaluation model of EVC is established. A day-ahead energy bidding model based on Stackelberg game is also established for VPP and EVC. Secondly, on this basis, the multi-temporal optimization model of VPP in the EFR market is proposed. To manage risks stemming from the uncertainties of WP, PV, and market price, the concept of conditional value at risk (CVaR) is integrated into the strategy, effectively balancing the bidding benefits and associated risks. Finally, the results based on operational data from a provincial electricity market demonstrate that the proposed strategy enhances comprehensive revenue by providing frequency regulation services and encouraging EV response scheduling.

Abstract:Rural electrification is a crucial component of the power system that requires urgent innovation and transformation to enhance electrification levels. However, various challenges hinder the progress in rural electrification, primarily due to remote locations and unique consumption patterns. To effectively coordinate the local energy distribution, an energy management framework utilizing peer-to-peer (P2P) based interactive operations is proposed, which minimizes the reliance on long-distance transmission while enhancing the rural electrification level. The proposed P2P-based energy management framework incorporates various distributed generation resources across rural areas, facilitating direct energy transactions between neighboring community-based villages. Additionally, the P2P energy trading is modeled as a Nash bargaining (NB) problem, which accounts for the allocation of network loss costs and operational state of the rural distribution network. To protect the privacy of individual villages, an improved adaptive alternating direction method of multipliers (AADMM) is proposed to solve the NB problem. The AADMM utilizes a local curvature approximation scheme during parameter updates, allowing for automatic adjustments of the fixed penalty parameter within the standard alternating direction method of multipliers (ADMM). This enhancement improves the convergence rates without requiring central oversight. Simulation results demonstrate significant reductions in operational costs for both the overall network and individual village participants. The proposed P2P-based energy management framework also enhances the bus voltage stability and reduces the line transmission power, thereby further enhancing rural electrification levels. The adaptability and extensibility of this framework are further validated using the IEEE 33-bus and 118-bus distribution systems. Additionally, the AADMM shows higher convergence rates compared with the standard ADMM.

Abstract:This paper presents a holistic pricing and distributed scheduling framework for multi-microgrid system (MMGS) that considers the supply‒demand relationships of the coupled electricity‒carbon market to promote collaborative market trading within the MMGS for economic and environmental benefit improvement. Initially, an operation model of each microgrid is developed by synthetically considering electricity-carbon operational constraints related to generation units and energy storage units. Then, a collaborative optimization strategy of the MMGS is established according to the Nash bargaining game (NBG) model with the objective of maximizing overall operational revenue. To determine the trading schedule, an accelerated prediction-correction-based alternating direction method of multipliers (PCB-ADMM) algorithm is employed to derive the optimal scheduling strategy of MMGS in a distributed manner, ensuring the privacy preservation of individual microgrids. For electricity-carbon pricing, a supply-demand ratio (SDR) based pricing strategy is proposed to dynamically update electricity and carbon allowance prices, which fundamentally guides and incentivizes each microgrid to trade within the MMGS preferentially rather than with an upstream distribution network. Finally, a study case verifies the effectiveness of the proposed framework in enhancing the operation economy and environmental friendliness of the entire MMGS.

Abstract:Over the past decade, bidding in electricity markets has attracted widespread attention. Reinforcement learning (RL) has been widely used for electricity market bidding as a powerful artificial intelligence (AI) tool to make decisions under real-world uncertainties. However, current RL-based bidding methods mostly employ low-dimensional bids (LDBs), which significantly diverge from the N price-power pairs commonly used in current electricity markets. The N-pair bid format is denoted as high-dimensional bid (HDB) format, which has not been fully integrated into the existing RL-based bidding methods. The loss of flexibility of current RL-based bidding methods could greatly limit the bidding profits and make it difficult to address the increasing uncertainties caused by renewable energy generation. In this paper, we propose a framework for fully utilizing HDBs in RL-based bidding methods. First, we employ a special type of neural network called the neural network supply function (NNSF) to generate HDBs in the form of N price-power pairs. Second, we embed the NNSF into a Markov decision process (MDP) to make it compatible with most existing RL algorithms. Finally, the experiments on energy storage systems (ESSs) in the Pennsylvania-New Jersey-Maryland (PJM) real-time electricity market show that the proposed bidding method with HDBs can increase the bidding flexibility, thereby increasing the profits of state-of-the-art RL-based bidding methods.

Abstract:The difficulty in capital recovery for distributed renewable energy operators (DREOs) and the high charging costs at electric vehicle charging stations (EVCSs) have long been significant challenges in power systems. Collaborative operation of DREOs and EVCSs can effectively address these challenges, yet few studies have approached incentivizing collaboration from the perspective of profit allocation. Therefore, this paper proposes a fair and efficient profit allocation method. Incorporating the Gauss-Legendre quadrature formula into the Aumann-Shapley value (GL-AS) method enables efficient calculation of the profit allocation of cooperative members. However, existing literature only discusses the profit allocation method of conventional power generation units, limiting its applicability. This paper addresses the problem of energy storage system (ESS) switching between charging and discharging in any time interval and the time-varying problem of renewable energy power output, thereby ensuring the efficiency of the solution process. Furthermore, a novel profit allocation adjustment model is provided through the adoption of triangular fuzzy comprehensive evaluation (TFCE). Finally, the effectiveness of the proposed profit allocation method is validated through numerical simulations in various scenarios.

Abstract:The increasing integration of renewable energy sources poses great challenges to the power system frequency security. However, the existing electricity market mechanism lacks integration and incentives for emerging frequency regulation (FR) resources such as wind power generators (WPGs), which may reduce their motivation to provide frequency support and further deteriorate the frequency dynamics. In this paper, a market scheduling and pricing method for comprehensive frequency regulation services (FRSs) is proposed. First, a modeling approach for flexible FR capabilities of WPGs is proposed based on the mechanism of inertia control and power reserve control. Subsequently, considering the differences in inverter control strategies, a novel system frequency response model with grid-following and grid-forming inverters is established. Combined with the automatic generation control, the frequency security constraints of the whole FR process are derived, and integrated into the market scheduling model to co-optimize the energy and FRSs. Finally, by distinguishing the contributions of various types of resources in different FR stages, a differentiated pricing scheme is proposed to incentivize producers with various regulation qualities to provide FRSs. The effectiveness of the proposed method is verified on the modified IEEE 6-bus system and the IEEE RTS-79 system.

Abstract:Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.

Abstract:The proliferation of electric vehicles (EVs) introduces transformative opportunities and challenges for the stability of distribution networks. Unregulated EV charging will further exacerbate the inherent three-phase imbalance of the power grid, while regulated EV charging will alleviate such imbalance. To systematically address this challenge, this study proposes a two-stage bidding strategy with dispatch potential of electric vehicle aggregators (EVAs). By constructing a coordinated framework that integrates the day-ahead and real-time markets, the proposed two-stage bidding strategy reconfigures distributed EVA clusters into a controllable dynamic energy storage system, with a particular focus on dynamic compensation for deviations between scheduled and real-time operations. A bi-level Stackelberg game resolves three-phase imbalance by achieving Nash equilibrium for inter-phase balance, with Karush-Kuhn-Tucker (KKT) conditions and mixed-integer second-order cone programming (MISOCP) ensuring feasible solutions. The proposed coordinated framework is validated with different bidding modes includes independent bidding, full price acceptance, and cooperative bidding modes. The proposed two-stage bidding strategy provides an EVA-based coordinated scheduling solution that balances the economic efficiency and phase stability in electricity market.

Abstract:Peer-to-peer (P2P) energy trading enables an efficient regulation of distributed renewable energy among prosumers, implicitly promoting low-carbon operation. This study proposes a novel P2P energy trading scheme with coupled electricity-carbon (E/C) market that co-optimizes both power and carbon emission flows. To facilitate the low-carbon operations in the market, we introduce a prosumer-driven carbon-aware distribution locational marginal price (PDC-DLMP) to serve as a pricing signal for the distribution system operator (DSO). To efficiently determine the optimal trading solutions, we adopt a two-layer data-driven approach. The first layer employs a reinforcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient (MATD3); the second layer uses a deep neural network (DNN) driven surrogate model, which is designed to map the PDC-DLMP signals, thereby eliminating the need for direct DSO intervention during market operation. This approach protects the physical model parameters of the distribution network and ensures multi-level privacy protection. Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market, demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.

Abstract:As the share of renewable generations (RGs) in power systems grows, the demand for peak regulation has increased, leading to higher associated costs. In this paper, we propose a mechanism for allocating peak regulation cost among RGs and distributing compensation among peak regulation resources (PRRs). This mechanism is integrated into a coordinated generation scheduling model to enhance the economic efficiency of independent system operators (ISOs) and incentivize PRRs. First, we propose a model for peak regulation cost of diverse PRRs. Next, we develop a method for constructing an RG output curve that facilitates peak regulation. The waveform difference between this constructed curve and the RG forecasted output curve is then calculated. In addition, we create a mechanism for peak regulation cost allocation and compensation distribution that incorporates the waveform difference, the peak regulation contribution of PRRs, and participant satisfaction as key indicators. We then establish a coordinated generation scheduling model using this mechanism, which is solved through the column-and-cut generation algorithm and rolling optimization. Finally, we conduct case studies based on an improved IEEE 30-bus test system and perform several comparative analyses to validate the effectiveness of the proposed mechanism and coordinated generation scheduling model.

Abstract:With the increasing number of distributed flexible resources with energy storage capabilities in virtual power plants (VPPs), the traditional market clearing model that only includes quantity and price bids cannot fully unlock their potential flexibility. In light of this, we propose a market clearing model for energy-constrained virtual power plants (EC-VPPs) based on distributionally robust chance-constrained optimization (DRCCO) with moment information. Furthermore, to address the uncertainty of EC-VPPs in the electricity market, a pricing strategy for EC-VPPs is proposed. This strategy helps quantify the impact of uncertainty in EC-VPPs on the system economy. The proposed market clearing model is reformulated as a tractable mixed-integer second-order cone programming (MISOCP) problem via a two-sided distributionally robust chance-constrained convex reformulation method. Numerical simulations verify that the proposed pricing strategy offers fair incentives for both reserve providers and uncertain sources, and delivers an effective market mechanism for the EC-VPPs.

Abstract:In the competitive energy market, energy retailers are facing the uncertainties of both energy price and demand, which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market. Particularly, the attractive multi-energy retail packages are the key for retailers to increase their benefit. Therefore, combined with incentive means and price signals, five types of multi-energy retail packages such as peak-valley time-of-use (TOU) price package and day-night bundled price package are designed in this paper for retailers. The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower (MLMF) Stackelberg game, in which retailers are leaders and end-users are followers. Retailers make decisions to maximize the profit considering the conditional value at risk (CVaR) while end-users optimize the satisfaction of both energy comfort and economy. Besides, a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization (PSO) algorithm and CPLEX solver are applied to solve the optimization model for each participant (retailer or end-user). Numeral results show that the designed retail packages can increase the overall profit of retailers, and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.

Abstract:In response to the increasing penetration of volatile and uncertain renewable energy, the regional transmission organizations (RTOs) have been recently focusing on enhancing the models of pump storage hydropower (PSH) plants, which are one of the key flexibility assets in the day-ahead (DA) and real-time (RT) markets, to further boost their flexibility provision potentials. Inspired by the recent research works that explored the potential benefits of excluding PSHs ’ cost-related terms from the objective functions of the DA market clearing model, this paper completes a rolling RT market scheme that is compatible with the DA market. Then, with the vision that PSHs could be permitted to submit state-of-charge (SOC) headrooms in the DA market and to release them in the RT market, this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms, assisted by the proposed tri-level optimal SOC headroom model. Specifically, in the proposed tri-level model, the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs, and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two markets. Numerical case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

Abstract:Base station (BS) backup batteries (BSBBs), with their dispatchable capacity, are potential demand-side resources for future power systems. To enhance the power supply reliability and post-contingency frequency security of power systems, we propose a two-stage stochastic unit commitment (UC) model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks, in which the minimum backup energy demand is considered to ensure BS power supply reliability. The energy, operational reserve, and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy (VRE) scenarios. Furthermore, we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center (DC) and BSBBs. The BS model parameters are stored and processed locally, while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework, which safeguards BS privacy effectively. Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation, ensuring post-contingency frequency security, enhancing operational economics, and fully utilizing BSBBs ’ energy and power capacity. Besides, the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations. Additionally, numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.

Abstract:The offering strategy of energy storage in energy and frequency response (FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors (deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

Abstract:Photovoltaic (PV) and battery energy storage systems (BESSs) are key components in the energy market and crucial contributors to carbon emission reduction targets. These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading. This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets, with a specific focus on carbon reduction benefits. A two-stage bidding framework that optimizes the profit of PV and BESSs is presented. In the first stage, the day-ahead energy market takes into account potential real-time forecast deviations. In the second stage, the real-time balancing market uses a rolling optimization method to account for multiple uncertainties. Notably, a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control (AGC). This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market. This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error (ACE) occurs. The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.

Abstract:The push for renewable energy emphasizes the need for energy storage systems (ESSs) to mitigate the unpredictability and variability of these sources, yet challenges such as high investment costs, sporadic utilization, and demand mismatch hinder their broader adoption. In response, shared energy storage systems (SESSs) offer a more cohesive and efficient use of ESS, providing more accessible and cost-effective energy storage solutions to overcome these obstacles. To enhance the profitability of SESSs, this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models. We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS. Subsequently, we design various long-term contracts from both capacity and energy perspectives, establishing associated models and real-time rental models. Lastly, multi-time-scale resource allocation based on the decomposition of user demand is proposed. Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction, effectively reducing battery degradation, and leveraging the aggregation effect for SESS can generate an additional increase of 10.7% in net revenue.

Abstract:To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles (EVs) to participate in frequency regulation ancillary service market (FRASM), a decision aid model (DAM) is proposed. This paper presents three options for EV participating in FRASM, i.e., the base mode (BM), unidirectional charging mode (UCM), and bidirectional charging/discharging mode (BCDM), based on a reasonable simplification of users’ participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use (TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.

Abstract:Demand response transactions between electric consumers, load aggregators, and the distribution network manager based on the “combination of price and incentive” are feasible and efficient. However, the incentive payment of demand response is quantified based on private information, which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information. This paper proposes a method based on Vickrey-Clark-Groves (VCG) theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive declaration in the demand response transactions. Firstly, a demand response transaction framework with the price-and-incentive combined mode is established to illustrate the deceptive behavior in the demand response transaction. Then, the idea for eradicating deceptive declarations based on VCG theory is given, and a detailed VCG-based mathematical model is constructed following the demand response transaction framework. Further, the proofs of incentive compatibility, individual rationality, cost minimization, and budget balance of the proposed VCG-based method are given. Finally, a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.

Abstract:Energy storage (ES), as a fast response technology, creates an opportunity for microgrid (MG) to participate in the reserve market such that MG with ES can act as an independent reserve provider. However, the potential value of MG with ES in the reserve market has not been well realized. From the viewpoint of reserve provider, a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process, which differs from the model that MG with ES acts as a reserve consumer in most existing studies. Based on the proposed model, MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling. Considering that the proposed model is just in concept and cannot be directly solved, a multi-stage robust optimization reserve provision method is proposed, which leverages the structure of model constraints. Next, the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility. Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.

Abstract:A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems (RIESs) with multiple stakeholders. A two-level Stackelberg game model is established, with the RIES operator as the leader and the users as the followers. It considers the interests of the RIES operator and demand response users in energy trading. The leader optimizes time-of-use (TOU) energy prices to minimize costs while users formulate response plans based on prices. A two-stage distributionally robust game model with comprehensive norm constraints, which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage, is constructed to manage wind power uncertainty. Karush-Kuhn-Tucker (KKT) conditions transform the two-level Stackelberg game model into a single-level robust optimization model, which is then solved using column and constraint generation (C&CG). Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders’ interests and mitigating wind power risks.

Abstract:The inter-regional electricity market is instrumental in enhancing the economic efficiency, reliability, and integration of renewable generation within interconnected power systems. As the market boundary expands, the complexity and solution difficulties of market clearing increase rapidly. The presence of hybrid alternating current (AC)/direct current (DC) interconnector networks further compounds challenges in modeling trading paths and transmission tariffs. To address these issues, this paper proposes a path-aware market-clearing (PAMC) model tailored for the inter-regional electricity market, which accommodates the hybrid AC/DC interconnector network. A variable aggregation strategy is proposed to reduce the problem scale while ensuring equivalent optimality. In addition, a novel redundancy elimination method is developed to expedite the solution of the market-clearing problem. This framework utilizes envelope approximations of residual demand curves to identify bidding blocks that will not affect the marginal price. Corresponding decision variables are then constrained to their bounds to remove redundant information. Comprehensive case studies across different power system scales validate the superiority of the proposed PAMC model in improving social welfare, and verify the effectiveness of the proposed redundancy elimination method in accelerating the solution of the market-clearing problem.

Abstract:Considering the enormous potential application of autonomous mobility-on-demand (AMoD) systems in future urban transportation, the charging behavior of AMoD fleets, as a key link connecting the power system and the transportation system, needs to be guided by a reasonable charging demand management method. This paper uses game theory to investigate charging pricing methods for the AMoD fleets. Firstly, an AMoD fleet scheduling model with appropriate scale and mathematical complexity is established to describe the spatio-temporal action patterns of the AMoD fleet. Subsequently, using Stackelberg game and Nash bargaining, two game frameworks, i.e., non-cooperative and cooperative, are designed for the charging station operator (CSO) and the AMoD fleet. Then, the interaction trends between the two entities and the mechanism of charging price formation are discussed, along with an analysis of the game implications for breaking the non-cooperative dilemma and moving towards cooperation. Finally, numerical experiments based on real-world city-scale data are provided to validate the designed game frameworks. The results show that the spatio-temporal distribution of charging prices can be captured and utilized by the AMoD fleet. The CSO can then use this action pattern to determine charging prices to optimize the profit. Based on this, negotiated bargaining improves the overall benefits for stakeholders in urban transportation.