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Stochastic optimization for retailers with distributed wind generation considering demand response

Journal of Modern Power Systems and Clean Energy 2018年第4期6卷 733-748页

作者： hessam golmohamadi Reza KEYPOUR Faculty of Electrical and Computer Engineering Semnan University

In this paper, a multi-stage stochastic model is presented for a renewable distributed generation(RDG)-owning retailer to determine the trading strategies existing in a competitive electricity market. Uncertainties associated with wholesale electricity market price, clients' consumption and power output of wind resources are considered through auto regressive integrated moving average(ARIMA) approach. In the proposed method, three trading floors are addressed for the retailer to hedge against the uncertainties. In the first stage, the retailer participates in day-ahead market to supply the clients and in the second stage, intraday market is addressed to allow the retailer to modify the schedule of its clients' consumption/RDG production. Due to unfavorable uncertainties, especially in renewable power production, real-time market is considered in the third stage to diminish the uncertainty at power delivery time. Cost function of wind resources considering capital, operation and maintenance(O&M) cost is incorporated in the objective function to increase the applicability of the mechanism. The proposed approach is formulated for risk-averse and risk-taker retailer through conditional value at risk(CVaR) approach. In order to study the impact of retail strategies on consumption patternand consumers' electricity bills, time-of-use(TOU)demand response programs are discussed in this *** the problem, the mixed integer non-linear programming(MILNP) problem is transformed into mixed integer linear programming(MILP) by jointly using decomposition and disjunctive constraints. Finally, a case study containing wind power resources, energy storage system and retailer is considered to analyze the proficiency of the proposed approach.

关键词： Renewable Retailer Stochastic Uncertainty Demand response

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Risk-based maintenance scheduling of generating units in the deregulated environment considering transmission network congestion

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Journal of Modern Power Systems and Clean Energy 2014年第2期2卷 150-162页

作者： hessam golmohamadi Maryam RAMEZANI Amir BASHIAN Hamid FALAGHI Faculty of Electrical and Computer Engineering The University of BirjandBirjandIran Department of Power Electrical Engineering Ferdowsi University of MashhadMashhadIran

In restructured power systems,the traditional approaches of unit maintenance scheduling(UMS)need to undergo major changes in order to be compatible with new competitive *** the maintenance on generating units may decrease the security level of transmission network and result in electricity shortage in power system;as a result,it can impose a kind of cost on transmission network as called security ***,taking off line a generating unit for performing maintenance can change power flow in some transmission lines,and may lead to network *** this study,generating unit maintenance is scheduled considering security and congestion cost with N-1 examination for transmission lines random *** proposed UMS approach would lead to optimum operation of power system in terms of economy and *** achieve this goal,the optimal power flow(OPF)compatible with market mechanism is ***,the electricity price discovery mechanism as locational marginal pricing(LMP)is restated to analyze the impacts of UMS on nodal electricity *** security and congestion cost simultaneously,this novel approach can reveal some new costs which are imposed to transmission network on behalf of generation units;as a result,it provides a great opportunity to perform maintenance in a fair environment for both generating companies(GenCo)and transmission companies(TransCo).At the end,simulation results on nine-bus test power system demonstrate that by using this method,the proposed UMS can guarantee fairness among market participants including GenCos and TransCo and ensure power system security.

关键词： Congestion Security Transmission network Unit maintenance scheduling

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