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Optimal Electricity Procurement for Large Industrial Customers Considering Participation in Demand Response Programs

Angizeh, Farhad | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45597 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Fotuhi-Firuzabad, Mahmud; Rajabi-Ghahnavieh, Abbas
  7. Abstract:
  8. Advancements in smart grid technologies have made it possible for consumers to play an effective role in electricity markets. One of the prominent options for consumers is participating in Demand Response (DR) programs. DR offers incentives designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized. Large industrial consumers who are potentially capable of directly participating into the wholesale day-ahead electricity markets, can make financial benefits by choosing appropriate strategies to take part in hourly DR programs. As well as participating in DR programs which can be used to manage electricity costs, minimizing electricity procurement cost from available electricity sources should be considered as well. In this context, the proposed approach, which is in the mixed-integer linear programming format, considers optimal integration of various DR strategies for hourly load reduction including load shifting, utilization of onsite generation, and energy storage systems together, as well as energy procurement problem that allows a large industrial consumer to optimally determine use of different electricity sources including bilateral contracts, a self-production facility of a limited size, and the electricity market.
    In addition, uncertainty of day-ahead electricity market prices is handled by treating them as stochastic variables that are characterized by an ARIMA model. The proposed stochastic approach is formulated as a two-stage stochastic mixed-integer programming (SMIP) problem with the first-stage for bilateral contracting and DR program participation, and the second-stage for scheduling self-production facility and market participation in hourly market prices scenarios. The scenario reduction method is also adapted to reduce the number of scenarios and computational burden. The objective is minimizing the expected value of the procurement cost considering DR rewards offered by ISOs, while limiting its volatility (risk) by incorporating risk aversion through the conditional value-at-risk (CVaR) methodology in the scheduling horizon.
    Numerical studies are conducted on a sample large industrial consumer. The results indicate that the customer can decrease its electricity procurement cost from €996292.73 to €981185.57 by choosing appropriate strategies to participate in hourly DR programs. Moreover, the results of proposed stochastic approach provide the expected costs ranges from €1227998.07 to €1302134.31 as a function of cost volatility which results in CVaR ranges from €1392542.09 to €1348384.74 respectively, which is required to carry out informed decision making.
  9. Keywords:
  10. Demand Response ; Stochastic Programming ; Conditional Value at Risk ; Mixed Integer Linear Programming ; Risk Measure Evaluation ; Autoregressive Integrated Moving Average (ARIMA) ; Electricity Procurement

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