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The emergence of power-to-gas stations (P2GSes) has provided opportunities to make an efficient use of surplus power generated from intermittent renewable energy, thus linking the natural gas and electricity networks as an integrated energy system. To build new P2GSes, new electricity feeders and natural gas pipelines should also be planned coordinately to support the operation of P2GSes. This paper presents a game-theoretic planning model for the integrated energy system (IES) consisting of the natural gas system, electricity system, and P2GSes. P2GSes are assumed to be independent participants in the IES in a deregulated market environment. The Nash bargaining theory is employed to formulate this cooperative planning model for the first time. In addition, the profitability potential of P2GSes to provide emission reduction and secondary reserve services is innovatively and thoroughly evaluated in the proposed planning model. The proposed Nash bargaining planning model considering ancillary service is then applied to the IES comprising a coupled 24-bus electricity and 20-bus natural gas system. Case studies validate its effectiveness and we have compared the proposed model with two typical planning models including the traditional centralized planning and sequential planning models. The simulation results show that the proposed planning model can achieve the most fair and Pareto-efficient payoff allocation for the three independent participants and also achieve a good IES system performance. It can be concluded that our model can not only help to promote the popularization of P2G technology, but also enhance the cooperation among the P2GSes, the electricity system and the natural gas system.

A bstract Renewable energy producers such as wind power producers (WPP) and electric vehicle (EV) aggregators are playing an increasingly important role in the electricity market as their large capacity could strategically influence the electricity price. This paper proposes a bi-level stochastic optimization model of offering strategy for an aggregated WPP-EV hybrid power plant (HPP) as a price maker in the day-ahead (DA) market while considering the uncertainties of the energy production and spot price in the real-time (RT) market. While the HPP's profits is maximized in the upper level of the proposed model with the use of conditional-value-at-risk (CVaR) to manage the risk of expected revenues, the social welfare from the perspective of the grid is maximized in the lower level. The formulated bi-level model is first transformed into a single-level mathematical program with equilibrium constraints (MPEC) and then further transformed into a mixed integer linear programming (MILP) problem for solution. Simulation results have demonstrated the effectiveness of the proposed HPP model with strategically bidding price to increase profits and reduce volatility of profits by considering the risk-metric.

In this paper, a comprehensive two-stage robust distribution system operation model is proposed by adjusting the charging of electric vehicle aggregators (EVAs) and curtailing loads. Because uncertainties in EVA charging demands are involved in the second stage of the adopted two-stage framework, distributionally robust optimization is used to improve the average economic performance of the proposed model, and security of distribution system operation is guaranteed by applying the Farkas lemma and robust optimization. The proposed model is solved by iteratively adding optimality cuts and feasibility cuts through a novel constraint generation algorithm, whose mathematical Proof is provided. The case studies show that the proposed model is capable of properly handling EVA uncertainties and coordinating EVA charging and load curtailments. The optimal coordination depends on several key parameters including the cost coefficients of delaying EVA charging and curtailing loads, the limits on delaying EVA charging, the system load level, and the EVA uncertainty level.

The integration of renewable energy sources (RESs) in distribution networks has brought great challenges to the volt/var management due to their intermittency and volatility. This paper proposes a two-stage energy management framework of distribution networks to facilitate the accommodation of high wind energy penetration. In the proposed framework, the volt/var management problem is formulated and decomposed as a two-stage energy scheduling optimization model with different time frames considering the uncertainties of wind energy and load forecasts. In the first stage, a scenario-based stochastic day-ahead scheduling model is formulated to optimize the 24-h charging/discharging scheme of energy storage system (ESS) and power generation of diesel generator (DG) in order to minimize the expected operation cost. Based on the stochastic optimal scheduling results in the first stage, the second stage implements the multiobjective volt/var optimization (VVO) to determine the optimal real-time operation of volt/var control devices, considering the costs of adjusting the control devices (CACDs). The proposed method has been fully evaluated and benchmarked on a 69-bus distribution network under various operational scenarios to demonstrate its superiority on various performance metrics and further confirm its effectiveness and efficiency for distribution networks to accommodate a high penetration of wind energy.