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Demand Side Response Agreements

Chart 9. Coefficient of variation in “residual demand” from 2002 level. An effective way to do this can be to guide a pilot to one of your sites, in order to develop your benchmarks for different processes, such as. B as cooling, heating, ventilation and climate (HVAC) and other production or backup processes. This shows you the level of variation in energy demand, which is safe for your customer experience and product quality. The demand response offers the possibility of shifting demand from the top and thus reducing the corresponding energy costs, although the average daily consumption is not changed [104]. The spot market is characterized by different electricity prices from hour to hour, depending on fluctuations in electricity availability. The price of electricity for the day should be known 24 hours in advance. In addition to the volatility of the price of electricity, one of the main issues of the spot market is the obligation to charge and the derogatory sanctions associated with it. For large consumers, electricity suppliers impose an hourly electricity commitment every day and, if actual consumption deviates from the prevailing values, fines are often in the same range as the net cost of electricity. In (Hadera et al., 2015), the authors take into account these multiple electricity contracts and the load gap problem to determine the optimal production plan. However, contract decisions (particularly burden obligations) are accepted and not optimized.

In this work, we face the challenge of at the same time determining the optimal electricity purchase strategy and production planning, including charging obligations, i.e. requiring consumers to consume the amount of electricity they will consume in a given period of time. Depending on the schedule, we can distinguish between the TOU commitment, which covers up to 3 months, and the daily hourly commitment in advance. Since decisions on the obligation to enter into electricity contracts must be made before actual electricity needs are known for the time horizon, it is essential to take into account uncertainties in the decision-making process. Current work in this direction (Zhang et al., 2016) integrates production planning and TOU contractual decisions through the application of two-tier stochastic programming (Birge and Louveaux, 2011) to model price and demand uncertainties. However, this work does not take into account the daily promise of electricity and the problem of the resulting load gap. To include daily engagement and the load gap problem, we propose a three-step stochastic programming approach. The variables of the first stage are the decisions concerning the obligation of electricity in the context of the TOU electricity contracts.

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