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This paper proposes a noncooperative game-based planning method for microgrid (MG) considering the interconnection between developing and developed privately-owned clustered MGs on an island. A noncooperative game with imperfect information is designed to regulate the decision-making in planning and operating strategies for rational MG owners. The optimization models for both developing and developed MGs are presented. Different levels of maturity of MG development are modeled by diverse objective functions and constraints, while the data privacy of privately-owned MGs is preserved through imperfect information and prior beliefs. To tackle the seasonality of renewable energy and load on an island, hydrogen storage is considered in the developing MG on the island. Diagonalization and realization are incorporated to develop an effective solving methodology in searching the mixed strategy equilibrium for each MG. The effectiveness of the proposed method is verified using comprehensive case studies. [ABSTRACT FROM AUTHOR]

This study proposes a multi-stage restoration strategy to solve the service restoration problem in distribution systems considering the uncertainty of outage duration. Furthermore, it also proposes a restoration index defined in terms of (i) priority of the load restored, (ii) number of switching operations, and (iii) estimated likelihood of the restoration solution to assist the utility in enhanced decision-making. Service restoration is formulated as a single objective optimisation problem considering the maximising of the restoration index as the only objective. A decentralised multi-agent system method is developed to solve the service restoration problem considering controlled islanding of distributed generators. The uncertainty of outage duration is estimated using the maximum entropy principle-based on the cause of the fault. Exhaustive case studies are implemented on a small 38 bus and a large 119 bus test distribution system to demonstrate the effectiveness of the proposed multi-stage restoration strategy to restore the system under outage duration uncertainty. [ABSTRACT FROM AUTHOR]

As a complement to the medium and long-term market, the spot market plays an important role in maintaining the security and stability of the power grid. However, as spot trading is more proximate to the actual operation of the power system, the virtual power plant (VPP) is exposed to greater volatility in renewable energy output as well as market prices. To enhance its market competitiveness, this paper constructs a two-step optimization model for VPP participation in the spot market. Based on long-term transaction, the energy storage power is distributed based on the comprehensive forecasting error of renewable energy output. This makes the VPP obtain the maximum profit in the day-ahead market. The trading deviation penalty is reduced by adjusting the energy storage operation plan in the real-time market. The conditional value at risk is used to measure the risk of a trading strategy. The profit of VPP participation in the spot market is analyzed under different risk levels, thus providing a basis for decision makers with different risk appetites. The results show that: (1) The sensitivity of the growth rates of E t VaR α and E t CVaR α to risk level shows an opposite trend. And there is an excess of arbitrage power at low risk level. (2) Although the total cost decreases as the risk level increases, the actual profit in the spot market tends to increase and then decrease. The risk level of the VPP should be set at about 0.4 to make full use of energy storage and obtain the maximum market benefit. (3) The energy storage power can be distributed more accurately according to the forecasting error of renewable energy. This not only enhances its arbitrage ability, but also ensures its regulation ability, thus improving the overall benefit of VPP at different risk levels. (4) The sensitivity of the trend in the unit profit of traded quantity in the spot market to the trading proportion in the medium and long-term market is not monotonic. • A two-step optimal bidding strategy for VPP in multimarket is developed. • The forecasting errors of wind and PV are comprehensive analyzed. • The ESS power is distributed into reserve power and arbitrage power. • Options for VPP with different risk attitudes are analyzed. • Sensitivity analyzes of trading proportion in the MLM are performed. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) plays an important role in managing distributed energy by integrating renewable energy sources, energy storage systems and dispatchable loads. It can not only provide peak regulation services as good flexible resources, but also participate in the electricity market for additional profit. This paper presents a multimarket model to develop an optimal bidding strategy for VPP. To enhance the effectiveness of demand response, a fixed time of use price is converted into a dynamic response price. The integrated consumption satisfaction is quantified from both comfort and economy perspectives. Multi-objective optimization is carried out to maximize market profit of VPP as well as consumer satisfaction. The results show that: (1) Compared to time of use prices, VPP's profit and consumers' satisfaction level increased by 12.46% and 3.26% respectively under dynamic response prices. (2) The increase in the market profit of VPP is accompanied by a gradual decline in integrated consumption satisfaction. There are two inflection points in the process of decline, occurring at satisfaction levels of 1.01 and 0.98 respectively. (3) The multi-objective optimization strategy can achieve a win-win situation for both its operator and the internal consumers. • An optimal bidding strategy for VPP in multimarket is developed. • A dynamic response price machoism is proposed based on renewable energy output. • Consumers' satisfaction is comprehensively considered by comfort and economy. • The profit and satisfaction are in equilibrium by multi-objective optimization. [ABSTRACT FROM AUTHOR]