Energy Management of a Cluster of Interconnected Price-Responsive Demands

In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm is proposed that converges to the optimal solution in a practical number of iterations and with a limited communication overhead. Furthermore, this approach allows for a very intuitive economics interpretation that explains the algorithm iterations in terms of "supply-demand model" and "market clearing." Numerical results are given in terms of convergence rate of the algorithm and attained costs for different network topologies.

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“…Inequality (23) guarantees that E j,i is positive. Following, by injecting (24) into (21a), it turns out that η must satisfy…”

Section: Proof Of Casementioning

confidence: 99%

“…7) that allows us to compute the energies bought from neighbor microgrids according to (24), as stated by Case 3. Also, knowing η and recalling that χ i (·) is the inverse function of C i (·), (21a) allows us to compute the generated energy…”

Section: Proof Of Casementioning

confidence: 99%

Distributed Energy Trading: The Multiple-Microgrid Case

Gregoratti

Matamoros

2015

IEEE Trans. Ind. Electron.

293

167

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“…The optimization problems (3)- (14) derive the N h bidding curves, one for each period of time t of the time horizon, with N dp pairs of possible values of energy-price, which correspond to the scenarios of the energy prices. It must be remarked that constraints related to shiftable demand (4) and batteries (6) and (7) are fulfilled for every scenario, but it may happens that after the market clearing, the committed energy for each period of time corresponds to different scenarios, and thus, those constraints will not be satisfied. A possible solution is to perform an adjustment process for rescheduling the flexible power, as explained below, but other solutions could be followed.…”

Section: Day-ahead Marketmentioning

confidence: 99%

Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

2017

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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.

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“…In [7], a robust optimization approach is proposed for the optimal energy management of a cluster of flexible loads under uncertainty in RES output and energy price. Historical data and a forecasting tool based on autoregressive models are employed to identify upper and lower bounds within a given confidence interval for the uncertain variables.…”

Section: A Backgroundmentioning

confidence: 99%

A stochastic dual dynamic programming approach for optimal operation of DER aggregators

Fatouros

Konstantelos

Papadaskalopoulos

et al. 2017

2017 IEEE Manchester PowerTech

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Abstract-The operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees.

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Energy Management of a Cluster of Interconnected Price-Responsive Demands

Cited by 91 publications

References 19 publications

Distributed Energy Trading: The Multiple-Microgrid Case

Distributed Energy Trading: The Multiple-Microgrid Case

Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

A stochastic dual dynamic programming approach for optimal operation of DER aggregators

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