In countries with an extended use of district heating (DH), the integrated operation of DH and power systems can increase the flexibility of the power system, achieving a higher integration of renewable energy sources (RES). DH operators can not only provide flexibility to the power system by acting on the electricity market, but also profit from the situation to lower the overall system cost. However, the operational planning and bidding includes several uncertain components at the time of planning: electricity prices as well as heat and power production from RES. In this publication, we propose a planning method based on stochastic programming that supports DH operators by scheduling the production and creating bids for the day-ahead and balancing electricity markets. We apply our solution approach to a real case study in Denmark and perform an extensive analysis of the production and trading behavior of the DH system. The analysis provides insights on system costs, how DH system can provide regulating power, and the impact of RES on the planning.
Apart from the operational planning, planning methods have to consider the bidding in electricity markets. Nowadays, producers often base their offers on the given electricity price forecast, which is very volatile due to the variability of RES production and uncertain one day before the energy is delivered @cite_20 . Additionally, the production from RES in the DH system itself is uncertain. Consequently, tools that optimize the operation of DH systems and propose bidding strategies need to consider the uncertainty given by price and production. Despite several bidding strategies for price-taker power producers in the day-ahead market have been proposed, (see references in @cite_12 ), the authors in @cite_24 demonstrate that under high uncertainty of electricity prices the use of stochastic programming @cite_2 for creating bidding curves for the day-ahead market renders good solutions that consider the uncertainty involved in the bidding process. Based on the representation of the uncertain electricity prices as scenarios, the authors use non-anticipativity constraints that order the bids presented to the market in a step-wise manner to create price dependent bids.
@cite_24 @cite_12 @cite_20 @cite_2
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Abstract This paper proposes a stochastic and dynamic mixed-integer linear program (SD-MILP) for optimal coordinated bidding of a risk-averse profit-maximizing hydropower producer. The day-ahead, intra-day, and real-time markets are considered. To model and predict day-ahead, intra-day, and real-time prices, the Holt–Winter (HW) and the Generalized Autoregressive Conditional Heteroscedastic (GARCH) predictors are combined using a proposed Markov switch. The discrete behavior of intra-day and real-time prices is modeled as different Markov states. The proposed Markov-based HW–GARCH model with a standard scenario generation-reduction technique is used to capture the uncertainty in day-ahead, intra-day, and real-time prices. The time-dependent conditional value at risk (T-CVaR) is proposed to model the risk of trading in different considered markets. The convex combination of the expected profit and T-CVaR is used as the objective of SD-MILP. The Markov-based HW–GARCH is modeled in Matlab and the SD-MILP is coded in GAMS. The Markov-based HW–GARCH predictor and the SD-MILP are used to develop the bidding curve of a three-reservoir hydropower producer using the electricity prices from the Nordic power market. To further examine the developed models a seven-reservoir hydropower producer is also studied. For these two cases, the coordinated bidding curves are derived and discussed. This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand) and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES) generation). The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets. This paper determines the optimal bidding strategy of an electric vehicle (EV) aggregator participating in day-ahead energy and regulation markets using stochastic optimization. Key sources of uncertainty affecting the bidding strategy are identified and incorporated in the stochastic optimization model. The aggregator portfolio optimization model should include inevitable deviations between day-ahead cleared bids and actual real-time energy purchases as well as uncertainty for the energy content of regulation signals in order to ensure profit maximization and reliable reserve provision. Energy deviations are characterized as “uninstructed” or “instructed” depending on whether or not the responsibility resides with the aggregator. Price deviations and statistical characteristics of regulation signals are also investigated. Finally, a new battery model is proposed for better approximation of the battery charging characteristic. Test results with an EV aggregator representing one thousand EVs are presented and discussed. Abstract This paper provides a technique based on stochastic programming to optimally solve the wind power problem faced by the uncertainty. Uncertainties regarding the wind availability, market prices, and balancing energy needs are considered throughout the paper. The objective of this paper is to derive the best offering strategy for a wind power producer in a Short-Term electricity market, while limiting the risk of expected profit and required reserve due to wind speed forecast volatility. Risk aversion is explicitly modeled using the conditional value-at-risk methodology. ARIMA techniques are used to predict next-day electricity prices and wind speed forecast. For more performance the probability distribution function of the error between forecasted value and realized value to scenario generation is used. A realistic of numerical case studies demonstrates the interest and the effectiveness.