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Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production
- Ferruzzi, Gabriella, Cervone, Guido, Delle Monache, Luca, Graditi, Giorgio, Jacobone, Francesca
- Energy 2016 v.106 pp. 194-202
- algorithms, case studies, decision making, decision support systems, econometric models, electricity, energy, energy costs, hospitals, markets, power plants, probability distribution, renewable energy sources, risk, risk management, uncertainty
- The power grid consists of various electrical components and of multiple levels: transmission HV (High Voltage), distribution in MV (Medium Voltage) and distribution in LV (Low Voltage). In this framework, the MGs (Micro Grids) are classified as a distribution grid, usually in LV, able to provide services both in autonomous (island mode) and in grid connected mode.MGs are composed by traditional and renewable energy power plants, storages and loads and, due to their limited capacity, generally the main applications are on residential level (e.g., campus, hospitals, hotels, sport centers, commercial location). Different components, design and rules are defined by the manager of MG: in this work, there is a prosumer which aggregates the capacity of different components and buys or sells, for each hour, power from/to the grid with upper level voltage.In this paper, a decision making model to formulate the optimal bidding in the Day-Ahead energy market and to evaluate the risk management for a LV grid-connected residential MG, taking into account the uncertainty of renewable power production, i.e., PV (photovoltaic), is proposed. Several investigators have analyzed the role played by MGs into the deregulated electricity market, their contribution to energy price reduction and to the reliability system increase, as well as their impact on the best strategy devising to minimize operating costs.Although in literature it is possible to find similar decision support models, the use of uncertainty evaluation to make decisions and to participate in a deregulated energy market is at the present an important open research issue.The uncertainty can be expressed in many different ways, either qualitative or quantitative, and it is possible to generate a reasonable measure of uncertainty by various methods.In this work an original approach based on AnEn (Analog Ensemble) method to estimate the uncertainty linked to the energy provided by PV plant own to the MG is presented.The AnEn is able to estimate the pdf (probability density function) of forecasts solutions by sampling the uncertainty in the analysis and running a number of forecast from perturbed analysis. The analogs generated become the input of our optimization model.Based on a genetic algorithm, the economic model is applied to a heterogeneous residential MG with traditional different power plants and RES (Renewable Energy Sources), i.e., PV, evaluating different prosumer risk tolerances (adverse, neutral and incline). Developed methodology can aid the decision maker to understand the potential impact of a wrong decision throughout information included in a forecast concerning renewable power production.The effectiveness of the proposed methodology is assessed through the analysis of a case study consisting of a grid connected residential MG. The obtained results show different optimal bids depending on the risk adversity with respect to the uncertainty of PV power production, and how PV energy production can be integrated with optimal results in a MG if the prosumer's strategy takes into account the uncertainty linked to the energy output.