In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. ''Budget of uncertainty'' and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.
Distributed energy resources (DERs) are deployed vastly to reduce carbon emission, improve power quality and maintain the reliability of distribution systems. With the introduction of new players, such as prosumers, which are constructed with DERs, distribution system operators (DSOs) are facing changes in the retail electricity market. Prosumers need a well‐defined strategic bidding mechanism to maximize their operation revenue, while DSOs need a new market clearing mechanism for the changed retail electricity market. Thus, an innovative game‐theoretic market framework for a prosumer‐centric retail electricity market is proposed. A bilevel algorithm is adopted to model new features of DSOs, utility companies and prosumers. The supply function equilibrium model, Nikaido–Isoda functions, and relaxation algorithms are applied to analyse the competition among key participants in a retail electricity market. Extensive simulation results are employed to illustrate and validate the effectiveness of the proposed framework for bidding strategies of prosumers with a retail electricity market. Specifically, the strategy with dumping‐bid or abnormal‐bid from a prosumer is suppressed by the market operator in the model. Moreover, the sensitivity analysis shows that the proposed framework can handle various numbers of prosumers in the retail electricity market with reasonable computational time and convergence rate.
Demand response has been implemented by distribution system operators to reduce peak demand and mitigate contingency issues on distribution lines and substations. Specifically, the campus-based commercial buildings make the major contributions to peak demand in a distribution system. Note that prior works neglect the consumers' comfort level in performing demand response, which limits their applications as the incentives are not worth as compared to the loss in comfort levels for most time. Thus, a framework to comprehensively consider both operating costs and comfort levels is necessary. Moreover, distributed energy resources are widely deployed in commercial buildings such as roof-top solar panels, plug-in electric vehicles, and energy storage units, which bring various uncertainties to the distribution systems, i.e., (i) output of renewables; (ii) electricity prices; (iii) arrival and departure of plug-in electric vehicles; (iv) business hour demand response signals and (v) flexible energy demand. In this paper, we propose an optimal demand response framework to enable local control of demand-side appliances that are usually too small to participate in a retail electricity market. Several typical small demand side appliances, i.e., heating, ventilation, and air conditioning systems, electric water heaters and plug-in electric vehicles, are considered in our proposed model. Their operations are coordinated by a central controller, whose objective is to minimize the total cost and maximize the customers' comfort levels for multiple commercial buildings. A scenario-based stochastic programming is leveraged to handle the aforementioned uncertainties. Numerical results based on the practical data demonstrate the effectiveness of the proposed framework. In addition, the trade-off between the operation costs of commercial buildings and customers' comfort levels is illustrated.
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