Distributionally robust co-optimized offering for transactive multi-energy microgrids

Zhang, Kai; Han, Xuejiao

doi:10.1016/j.ijepes.2022.108451

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…For the convenience of modelling, the fixed load can be viewed as a kind of non‐dispatchable DER, including non‐flexible household power consumption [8]. The set of fixed loads is represented by Σ [25].…”

Section: Modelling Of the Mg With Dersmentioning

confidence: 99%

“…This testing system 1 is obtained from ref. [8], consisting of six nodes, one battery energy storage system, two PV generators, one flexible building, and one mixed‐commercial‐residential flexible load, as shown in Figure 3. The market data are obtained from the public Singaporean wholesale market and the forecasted prices for energy and contingency reserve are given in Figure 4.…”

Section: Case Studymentioning

confidence: 99%

“…In terms of the total operation cost, the proposed DRL method yields a solution with a cost of 692.2$. Table 1 compares the numerical results from different methods, including stochastic optimization (SO) and distributionally robust optimization (DRO) [8]. It can be observed that the DRO provides the most conservative results since it focuses on the worst‐case scenario.…”

Section: Case Studymentioning

confidence: 99%

“…Despite its higher computational efficiency, it tends to provide over-conservative solutions. To overcome the limitations of both stochastic and robust optimization methods, distributionally robust optimization (DRO) has been applied to power system operation and planning problems [8][9][10][11][12][13], including the microgrid bidding problem [8]. DRO can accommodate estimation errors in exact probability distributions while providing less conservative solutions than robust optimization methods [14].…”

Section: Introductionmentioning

confidence: 99%

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A data‐driven method for microgrid bidding optimization in electricity market

Yan,

2023

Energy Conversion and Econom

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This paper presents a deep reinforcement learning based data‐driven solution to the microgrid bidding in the electricity market considering offers for the reserve market. The framework, based on the Markov decision process, models the microgrid's participation in the electricity market at different stages, including bidding, market‐clearing, and reserve activation. The problem is split into two stages: day‐ahead submission and real‐time market period, and the proposed method mainly focus on the first stage. The state information from state‐space models of distributed energy resources serves as input for the policy network. A deep deterministic policy gradient is employed to train the network and produce a deterministic bidding strategy. The second stage can then adjust this strategy based on the results from the first stage. The method is validated with real‐world microgrid systems and data from the Singapore spot market.

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Section: Modelling Of the Mg With Dersmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A data‐driven method for microgrid bidding optimization in electricity market

Yan,

2023

Energy Conversion and Econom

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“…In recent years, a few studies have applied distributionally robust optimization (DRO) to solve uncertainty optimization problems in energy systems [15]. Combining the merits of the SP and RO, the DRO is to implement the scheduling strategy under the worst uncertainty probability distribution in the ambiguity set.…”

Section: Introductionmentioning

confidence: 99%

Flexible operation of a CHP‐VPP considering the coordination of supply and demand based on a strengthened distributionally robust optimization

Fang

Liu

2023

IET Control Theory & Appl

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Due to the generation variability, the growing capacity of renewable energy has posed unprecedented challenges to ensure the security of power system operation. Here, a two‐stage strengthened distributionally robust optimization (DRO) scheme is proposed for theself‐scheduling of a combined heat and power virtual power plant (CHP‐VPP) over a coupled electric power network (EPN) and district heating network (DHN). The CHP‐VPP operator maximizes its profits in the day‐ahead market and minimizes its cost in the real‐time market under the worst‐case realization of the uncertainties. Instead of assuming that the uncertainties follow known probability distributions or confidence bounds, a strengthened ambiguity set based on moment information and Wasserstein metric is built to provide more accurate characterizations of the true probability distribution of uncertainties. In addition, in order to enhance the flexibility of the system, a HOMIE model considering indoor activities and outside temperatures of each building is built to satisfy the comfortable indoor temperature. To make the whole problem tractable, linearisation and duality theory are adopted, and then a tailored column‐and‐constraint generation algorithm is developed to solve the problem. The validity and applicability of the strengthened DRO scheme are verified by an IEEE 33‐bus EPN and 14‐node DHN.

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