Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To address these challenging issues, a forecast-then-estimate framework of edge learning is proposed for DSSE, leveraging large language models (LLMs) to forecast missing measurements and provide pseudo-measurements. First, natural language-based prompts and measurement sequences are integrated by the proposed LLM to learn patterns from historical data and provide accurate forecasting results. Second, a convolutional layer-based neural network model is introduced to improve the robustness of state estimation under missing measurement. Third, to alleviate the overfitting of the deep-learning-based DSSE, it is reformulated as a multitask learning framework containing shared and task-specific layers. The uncertainty weighting algorithm is applied to find the optimal weights to balance different tasks. The numerical simulation on the Simbench case is used to demonstrate the effectiveness of the proposed forecast-then-estimate framework.

As an important market entity, energy storage (ES) can participate in multiple electricity markets simultaneously and benefit from them. However, the current operation models for ES primarily concentrate on calculating market revenue, which fails to assess the risk and correlation of market prices accurately. Thus, this article designs an operation method for ES considering price risk and coupling in markets. First, a multiplexing scheme is designed for ES operation in multimarket, considering market price risk and market coupling. Second, generalized autoregressive conditional heteroskedasticity and exponentially weighted moving average models are introduced to precisely assess price risk, and a dynamic Copula method is designed to reflect the coupling coefficient. Furthermore, it introduces the concept of expected premium calculated through real options theory, allowing for more refined profit maximization strategies under positive fluctuating market conditions. Demonstrated with a real PJM multimarket-based framework, the proposed method leads to an 18.2% increase compared to the traditional portfolio method, which provides a new business model for ES to unlock its potential value.

Network reconfiguration (NR) is a highly complex combinatorial problem with discrete and nonlinear characteristics. With the expansion of the distribution network (DN), solving the NR problem faces the challenge of high dimensions. In this article, we propose a structural decomposition approach (SDA), where the NR problem is suitably allocated to three processes: partition, reconfiguration of equivalent networks, and merging. According to the loop in the original DN, the loop-oriented network partition model is proposed to divide the original DN into multiple equivalent networks, including a loop region and a compressed region. The reconfiguration model is built for the equivalent networks, and the solutions for the loop region and compressed region can be obtained. Then the merging model with the correction method is proposed to merge all reconfiguration solutions of equivalent networks, deal with the inconsistent solutions of different equivalent networks, and obtain the optimal reconfiguration solution of the original DN. Numerical case studies were conducted on the IEEE 33-bus and 119-bus DNs. Compared with other heuristic algorithms for solving NR problem, SDA reduces computation time by 70% while ensuring the optimality of NR strategies. These results demonstrate the effectiveness and exceptional performance of the proposed method.

The renewable energy community (REC) is a prosperous scheme to promote distributed renewable resources in the city and suburban areas. Although energy storage (ES) is essential to smooth the volatility of REs, it is costly for small-scale REC investing ES to increase its profits in the frequency regulation market (FRM). Thus, renting ES capacity is an alternative way to boost the development of REC. With rented ES capacity, there are three barriers: 1) the correspondence between the ES capacity rental mode and the frequency regulation mileage performance; 2) the cooperated operation method between REC and ES; 3) the uncertainties of renewables in the FRM. This article proposes an operation strategy for REC with rented ES capacity in FRM under renewable and price uncertainties. First, considering regulation capacity and performance prices, an FRM is modeled with regulation up and down services. Second, the ES capacity rental model is designed with normal rental mode and extra rental mode based on the low and high ratios between the mileage and capacity. Then, to unify the output of REC with capacity rental in FRM, a cooperated operation method is proposed considering the self-scheduled curtailment of renewables and the interaction between the renewables and ES. The total profits of REC are maximized with the uncertainties of REC output, loads and prices via the weighted sum of the value in multi scenarios. The proposed method is demonstrated with an REC and the results show that the total profits are enhanced by nearly 2 times with the proposed method.

The real-time electrical equipment management, such as renewable energy, controllable loads, and storage units, plays a key role in low-carbon operation of smart industrial park. Digital twin (DT), which explores cloud-edge-device collaboration and artificial intelligence to establish accurate digital representation of physical equipment, is a cutting-edge technology to realize intelligent optimization of electrical equipment management. However, the practical implementation still faces reliability and communication efficiency problems, such as adverse impact of electromagnetic interference on DT reliability, high communication cost of DT model training, and uncoordinated resource allocation among cloud, edge, and device layers. We propose a Cloud-edge-device Collaborative reliable and Communication-efficient DT for lOW-carbon electrical equipment management named$\text{C}^{3}$-FLOW. It minimizes the long-term global loss function and time-average communication cost by jointly optimizing device scheduling, channel allocation, and computational resource allocation. Simulation results verify that$\text{C}^{3}$-FLOW performs superior in loss function, communication efficiency, and carbon emission reduction.

Distribution network reconfiguration is an effective method to face the problem of power fluctuation in the power system. Previous studies have focused on mathematical optimization techniques with complex modeling processes and heuristic algorithms with time-consuming solving processes to obtain the optimal reconfiguration strategy. In this article, a hybrid data-driven and model-based distribution network reconfiguration (HDNR) framework is proposed, where the model-based module includes model reduction and goal-oriented clustering to cluster the identical reconfiguration strategies. Here, the data-driven module is implemented through a long short-term memory network to learn the mapping mechanism between load distribution and optimal reconfiguration strategies. The model-driven module and the data-driven module are coupled through the proposed hierarchical network recovery process, which presents the reconfiguration results layer by layer. Finally, the numerical case study on the IEEE 33-bus, IEEE 119-bus, and IEEE 123-bus network shows the validity of the proposed HDNR framework. It is shown that the solution space is reduced, which contributes to reducing computation time and resources. Moreover, the obtained accuracy of the reconfiguration strategy is higher than most existing research even with limited data samples.

The heat–electricity integrated energy system (HE-IES) with energy hubs (EHs) and prosumers is the typical form of future energy systems. An EH can efficiently model the integration of different energy carriers and coordinate an integrated energy sharing system of multiple prosumers in HE-IES. In this article, a closed-loop framework is designed to integrate the contract theory and consensus algorithms for a bilevel heat–electricity energy sharing system. First, the energy sharing scheme between prosumers and an EH in the lower level energy system is formulated. Contract theory is utilized to maximize the profits of an EH under the information asymmetry scenario, and different contract items are designed for the electric and thermal networks. In particular, a set of new bidirectional contract items is proposed in the electrical network. Furthermore, a consensus-based energy sharing framework among various EHs and utility grid in the upper level system is developed and network constraints are considered. Two types of Lagrange multipliers are defined to decouple the electric and thermal network, and an interactive mechanism is designed to solve the problem. Finally, the simulation results verify the convergence of the bilevel closed-loop framework and the feasibility and performance of the scheme.

Distributed energy resources bring about challenges related to the participation of an increasing number of prosumers with strong social attributes in peer-to-peer (P2P) energy sharing markets, resulting in the increased complexity of socio-technical systems. Previous research has focused on energy sharing analysis based on rational games without considering the social attributes of prosumers, which are not typically used in real scenarios. In this article, an interdisciplinary P2P energy sharing framework that considers both technical and sociological aspects is proposed. It is based on prospect theory (PT) and stochastic game theory, in which the prosumers work as followers with subjective load strategies, while an energy sharing provider (ESP) serves as the leader with a dynamic pricing scheme. A subjective utility model with risk utility (RU) determined by PT is designed for prosumers, and a profit model for dynamic prices is suggested for ESP. Moreover, a solution algorithm that consists of interpolation and curve fitting to obtain the RU function, the aggregation of prosumers to a Markov decision process, and a differential evolution algorithm to solve the game are proposed to solve the problems of the “curse of dimensionality” and discreteness arising from the social attributes of prosumers. Numerical analysis reveals the results of the Stackelberg equilibrium and demonstrates the effectiveness of this method in terms of the social behavior of prosumers, i.e., radicalness when losing and conservatism when gaining.

This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids. In this framework, the interconnected microgrids not only fulfil the multienergy demands of with local hybrid biogas-solar-wind renewables, but also proactively trade their available multienergy and communication resources with each other for delivering secured and high quality of services. The multimicrogrid multienergy and communication trading is an intractable optimization problem because of their inherent strong couplings of multiple resources and independent decision-makings. The original problem is thus formulated as a Nash bargaining problem and further decomposed into the subsequent social multiresource allocation subproblem and payoff allocation subproblem. Furthermore, fully-distributed alternating direction method of multipliers approaches with only limited trading information shared are developed to co-optimize the communication and energy flows while taking into account the local resource-autonomy of heterogeneous microgrids. The proposed methodology is implemented and benchmarked on a three-microgrid system over a 24-h scheduling periods. Numerical results show the superiority of the proposed scheme in system operational economy and resource utilization, and also demonstrate the effectiveness of the proposed distributed approach.

This article proposes a new planning framework for optimal allocation of parking lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present article explicitly considers the uncertain implications of incentive policy on PEV owners' charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on an illustrative test system verify the effectiveness of the proposed model and the approach.

Peer-to-peer energy sharing in the distribution networks (DN) is an emerging issue with the large-scale development of photovoltaic (PV) prosumers. The DN can be classified into energy-shared regions (ESR) to enable the zonal energy trading. A Stackelberg-game-based energy-sharing framework is recommended for DN with multi-ESR, where the energy-sharing provider (ESP) works as a leader with dynamic pricing for multi-ESR, whereas PV prosumers serve as followers with the demand response's (DR) ability to choose an ESR to link and modify their flexible loads. A profit maximization model, along with multi-ESR pricing and a network usage fee, is designed for the ESP operation in this article. This involves a utility model with DR strategies, including ESR selection and load adjustment, which is proposed for the prosumers. Moreover, the presence and uniqueness of the Stackelberg equilibrium are being provided. Finally, through the use of a real system, the simulation results show that the ESP profit and prosumers can be increased whereas the impact of PV uncertainty and variability on the utility grid is reduced.

The paper proposes a two-stage energy sharing framework for a new prosumer microgrid with renewable energy generation, multiple storage units, and load shifting. In the first stage, a robust bilevel energy sharing model is formulated to provide a robust energy sharing schedule for prosumers and retailer overcoming the impact of the uncertainties of market prices and renewable energy. Through proper linearization techniques, the bilevel optimization problem is transformed into a single-level mixed integer linear programming problem that is practically solvable. In the second stage, an online optimization model is formulated for each prosumer to continually optimize its energy schedule at each hour according to the latest system state, and the proposed punishment mechanism is embedded for prosumers adjusting their previous energy sharing schedules. The simulation cases show the benefits of the energy sharing management framework.

Combined heat and power (CHP) is an important distributed generation type for the microgrids (MGs) with both thermal and electricity demand. In this paper, a multiparty energy management framework with electricity and heat demand response is proposed for the CHP-MG. First, in order to decide the electricity and thermal prices, an optimization profit model of a microgrid operator (MGO) is formulated including the cost of gas, the income of energy sold to the consumers, and the income of surplus electricity feed to the utility grid. The CHP system is operated in a hybrid mode by dynamically selecting the following-thermal-load mode and the following-electric-load mode. Moreover, for the building energy consumers, an optimization model is formulated containing the utility of electricity consumption, the expenditure of purchasing electricity/heat, and the comfortable degree of indoor temperature. The trading process between the MGO and consumers is designed as a one-leader$N$-follower Stackelberg game, and the existence and uniqueness of the Stackelberg equilibrium is proved. Finally, the case study of a CHP-MG system containing six building users is provided to show the effectiveness of the proposed method.

This paper proposes a stochastic programming approach based on Conditional Value at Risk (CVaR) to measure the impact of prediction errors brought about by the renewable energy and load on DC-linked multi-microgrids coalition in the distribution network so that the system scheduling operator can achieve the lowest day-ahead scheduling cost at a certain risk. Due to the prediction errors of the renewable energy and load, stochastic programming is suitable for modeling. The validity of the method is verified by the multi-microgrids coalition containing three DC-linked microgrids of the IEEE 33-node distribution system by using actual renewable energy and three typical load data. Finally, this paper gives a comparison between deterministic and stochastic scheduling.

With the development of microgrids (MGs), the energy management of MG is paid more attention. In this paper, a multi-party energy management framework for prosumers and operator considering combined heat and power (CHP) and heat storage with demand response (DR) is proposed. In particular, CHP can choose to obey the rule of heat-led mode or electricity-led mode to minimize the total operation cost. Based on the framework of alternating direction method of multipliers (ADMM), a distributed optimal scheduling model and an iterative algorithm for prosumers and operator is introduced, only expected amount of electric and heat and actual provided amount of electric and heat are required during the iterations, which protects the privacy information of prosumers and CHP operator. Finally, the effectiveness of the model and algorithm is verified via a case study.

Photovoltaic-assisted charging station (PVCS) is expected to be one of the important charging facilities for serving electric vehicles (EVs). In this paper, a type of solid-state transformer (SST) is introduced to the PVCS design and an autonomous energy management strategy (EMS) for SST is proposed. This study aims to develop an effective real-time EMS for PVCS participating in ancillary service of smart grid, and the rule-based decision-making method is utilized. Considering the dynamic classification of EVs, an energy-bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible resources. Moreover, considering the EBC results and power command from the aggregator, a charging power allocation algorithm is designed for power distribution of flexible EVs. By case study and experiment analysis, the proposed EMS is effective in real-time energy management and suitable for practical applications.

For microgrids with photovoltaic (PV) prosumers, the effective energy sharing management (ESM) is important for the operation. In this paper, a Stackelberg game approach for ESM is proposed. First, according to feed-in-tariff of PV energy, a system model of ESM is introduced, which includes the profit model of microgrid operator (MGO) and the utility model of PV prosumers. Moreover, an hour-ahead optimal pricing model of ESM is proposed. The model is designed based on Stackelberg game, where the MGO acts as the leader and all participating prosumers are considered as the followers. With the proof of equilibrium and uniqueness of the Stackelberg equilibrium, the MGO is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a billing mechanism is designed to deal with the uncertainty of PV energy and load consumption. By using the collected data from realistic PV-roofed buildings, the effectiveness of the model is verified in terms of the profit of MGO, the utilities of prosumers, and the net energy of the microgrid.

This paper mainly focuses on the energy management of microgrids (MGs) consisting of combined heat and power (CHP) and photovoltaic (PV) prosumers. A multiparty energy management framework is proposed for joint operation of CHP and PV prosumers with the internal price-based demand response. In particular, an optimization model based on Stackelberg game is designed, where the microgrid operator (MGO) acts as the leader and PV prosumers are the followers. The properties of the game are studied and it is proved that the game possesses a unique Stackelberg equilibrium. The heuristic algorithm based on differential evolution is proposed that can be adopted by the MGO, and nonlinear constrained programing can be adopted by each prosumer to reach the Stackelberg equilibrium. Finally, via a practical example, the effectiveness of the model is verified in terms of determining MGO's prices and optimizing net load characteristic, etc.