Zhihao Yang

Shared energy storage (SES) has garnered significant attention due to its potential to reduce costs and enhance utilization efficiency through multi-service provision. However, designing trading mechanisms that balance the interests of multiple stakeholders and optimizing capacity allocation across diverse services remain critical challenges. In this context, a nested game optimization strategy is proposed to optimize capacity rights trading and market bidding of SES for revenue maximization. First, optimization models for the market bidding and capacity rights trading of shared energy storage operator (SESO) and renewable energy producers (REPs) are formulated to derive their bidding strategies, which are subsequently submitted to and cleared within a day-ahead joint energy and frequency regulation market. The capacity rights trading process is modeled by a nested game utilizing a Stackelberg game (SG) for capacity pricing and generalized Nash equilibrium (GNE) for REPs competition. Then, a tri-level optimization method combining bisection and adaptive alternating direction method of multipliers (ADMM) relaxation is developed to solve the hierarchical multi-agent problem. The results of case studies demonstrate that the model increases SESO revenue by 10.7% and generates revenue growth for REPs, validating a mutually beneficial for multiple participants. Furthermore, the trading mechanism enhances the market competitiveness of REPs and improves overall social welfare.

This paper addresses the challenge of assessing photovoltaic (PV) hosting capacity in distribution networks while accounting for the uncertainty of PV output, a critical step toward achieving sustainable energy transitions. Traditional optimization methods for dealing with uncertainty, including robust optimization (RO) and stochastic optimization (SO), often result in overly conservative or optimistic assessments, hindering the efficient integration of renewable energy. To overcome these limitations, this paper proposes a novel distributionally robust chance-constrained (DRCC) assessment method based on Kullback–Leibler (KL) divergence. First, the time-segment adaptive bandwidth kernel density estimation (KDE) combined with Copula theory is employed to model the conditional probability density of PV forecasting errors, capturing temporal and output-dependent correlations. The KL divergence is then used to construct a fuzzy set for PV output, quantifying its uncertainty within specified confidence levels. Finally, the assessment results are derived by integrating the fuzzy set into the optimization model. Case studies demonstrate its effectiveness of the method. Key findings indicate that higher confidence levels reduce PV hosting capacities due to broader uncertainty ranges, while increased historical sample sizes enhance the accuracy of distribution estimates, thereby increasing assessed capacities. By balancing conservatism and optimism, this method enables safer and more efficient PV integration, directly supporting sustainability goals such as reducing fossil fuel dependence and lowering carbon emissions. The findings provide actionable insights for grid operators to maximize renewable energy utilization while maintaining grid stability, advancing global efforts toward sustainable energy infrastructure.

The installation of photovoltaic (PV) system for electrical power generation has gained a substantial interest in the power system for clean and green energy. However, having the intermittent characteristics of photovoltaic, its integration with the power system may cause certain uncertainties (voltage fluctuations, harmonics in output waveforms, etc.) leading towards reliability and stability issues. In PV systems, the power electronics play a significant role in energy harvesting and integration of grid-friendly power systems. Therefore, the reliability, efficiency, and cost-effectiveness of power converters are of main concern in the system design and are mainly dependent on the applied control strategy. This review article presents a comprehensive review on the grid-connected PV systems. A wide spectrum of different classifications and configurations of grid-connected inverters is presented. Different multi-level inverter topologies along with the modulation techniques are classified into many types and are elaborated in detail. Moreover, different control reference frames used in inverters are presented. In addition, different control strategies applied to inverters are discussed and a concise summary of the related literature review is presented in tabulated form. Finally, the scope of the research is briefly discussed.

Residential users (RUs) are the vital component of terminal energy consumption. The development and application of integrated energy system (IES) and smart homes has promoted RUs to actively take part in the trading with multi-energy provider (MEP) for its preferential energy prices and services. This paper proposes a pricing strategy of MEP by using a Stackelberg game-based bi-level programming model. In the upper level model, the adjustment coefficient of electric power price is optimized by MEP to increase the trading probability with RUs. In the lower level model, an integrated demand response (IDR) program is proposed for RUs to optimize the flexible loads in home energy management system (HEMS). Specially, a HEMS is composed of a smart interactive terminal, a micro combined cooling, heating, and power (mCCHP) system and multi-energy loads. Case study shows that, on one hand, the energy optimization based on IDR can help RUs manage their multi-energy loads and reduce the expected energy cost. On the other hand, the proposed price strategy of MEP can increase the trading probability, which promote more RUs to trade with MEP, thus increasing the MEP’s benefit by 12.29%. The research proves that the proposed strategy is a win-win strategy and it is efficient in the pre-decisionmaking progress for MEP in the energy trading market.

Load aggregators can help residents aggregate their loads to better participate in demand-side response in electricity market. For residents, the characteristics of flexible loads can be classified as adjustable and non-adjustable characteristics, transferable and non-transferable characteristics, interruptible and non-interruptible characteristics. Based on these characteristics, an optimal dispatching strategy of aggregated loads is proposed in this paper. For load aggregators, with the object of peak load shifting, an optimal dispatching strategy is developed. Then, to estimate the revenue of load aggregators, a settlement model is established. Case study results verified the validity of the proposed dispatching strategy, which can not only help distribution network reduce the peak and valley difference, but bring these load aggregators and residents extra benefits from the distribution network.

Inaccurate price prediction may cause improper charging/discharging schedules of battery energy storage (BES) in electricity market, which further leads to less or even negative market profits of BES. On this condition, uncertainty in market prices should be fully considered when making operation strategies. In this paper, focusing on an independently operated BES in day-ahead and hour-ahead markets, a multi-stage robust optimal operation strategy is formulated considering uncertainty in multi-periods market prices. The robust optimization model is formulated seeking to maximize market revenues of BES within a given confidence level of price deviation. The max-min model can be converted into a mixed-integer linear programming (MILP) problem which can be easily computable. Case study results demonstrated the validity of the presented robust operation strategy of BES.

This paper designs a multifunctional remote-controllable home system based load aggregation management system. On the one hand, this system combines the Load Aggregators with Smart Home and presents an effective and practical method to realize household load management. When the household loads are needed to adjust by load aggregators, the system can automatically control the load on the user side to maximize the utilization of energy and meet the demand of power dispatching. On the other hand, the system can make domestic appliance remotely controlled. To realize the functions mentioned above, the system consists of three parts: Raspberry Pi services, Remote-Controllable Home System and Load Aggregation System. The Raspberry Pi has been used to record related data and the control instructions from all users and respond to the requests from all clients. The Remote-Controllable Home System consists of client controller and client server. The client controller has been developed by HTML5 to collect the user's control instructions and present the related data. The client server has been coded to respond to the control instructions from the individual user and load aggregators. The whole system is manifested by experimental prototype, which can aggregate household loads and control them remotely.