Journal: CSEE Journal of Power and Energy Systems

Abbreviation

Publisher

Chinese Society for Electrical Engineering

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ISSN

2096-0042

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2022 - 20243
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Publications 1 - 3 of 3
  • Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming
    Item type: Journal Article
    Deng, Lirong; Zhang, Xuan; Yang, Tianshu; et al. (2024)
    CSEE Journal of Power and Energy Systems
    In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arbitrage value. However, this price-smoothing effect can result in significant external welfare changes by reducing consumer costs and producer revenues, which is not negligible for the community with energy storage systems. As such, we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare. To incorporate market interaction into the SDP format, we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices. Then we present an analytical SDP algorithm that does not require state discretization. Apart from computational efficiency, another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value. Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage. The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.
  • Consensus Clustering for Bi-objective Power Network Partition
    Item type: Journal Article
    Wang, Yi; Lebovitz, Luzian; Zheng, Kedi; et al. (2022)
    CSEE Journal of Power and Energy Systems
    Partitioning a complex power network into a number of sub-zones can help realize a 'divide-and-conquer' management structure for the whole system, such as voltage and reactive power control, coherency identification, power system restoration, etc. Extensive partitioning methods have been proposed by defining various distances, applying different clustering methods, or formulating varying optimization models for one specific objective. However, a power network partition may serve two or more objectives, where a trade-off among these objectives is required. This paper proposes a novel weighted consensus clustering-based approach for bi-objective power network partition. By varying the weights of different partitions for different objectives, Pareto improvement can be explored based on the node-based and subset-based consensus clustering methods. Case studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.
  • Personalized Federated Learning for Individual Consumer Load Forecasting
    Item type: Journal Article
    Wang, Yi; Gao, Ning; Hug, Gabriela (2023)
    CSEE Journal of Power and Energy Systems
    Installation of smart meters enables electricity retailers or consumers to implement individual load forecasting for demand response. An individual load forecasting model can be trained either on each consumer's own smart meter data or the smart meter data of multiple consumers. The former practice may suffer from overfitting if a complex model is trained because the dataset is limited; the latter practice cannot protect the privacy of individual consumers. This paper tackles the dilemma by proposing a personalized federated approach for individual consumer load forecasting. Specifically, a group of consumers first jointly train a federated forecasting model on the shared smart meter data pool, and then each consumer personalizes the federated forecasting model on their own data. Comprehensive case studies are conducted on an open dataset of 100 households. Results verify the proposed method can enhance forecasting accuracy by making full use of data from other consumers with privacy protection.
Publications 1 - 3 of 3