Security Constrained Unit Commitment Considering Ramp Rate and Transmission Line Losses Using Binary Particle Swarm Optimization Based on IEEE 30 Bus System

Aryani, Ni Ketut; Putra, Dimas Fajar Uman; Arifin, Elpha Aulia; Daroini, Ahmad Saad

doi:10.1109/isitia.2019.8937246

Cited by 5 publications

(1 citation statement)

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“…1 includes three thermal power units, two CHP units, a pumped storage power station, a heat storage tank and a wind turbine. The unit datas are from references [8] and [9] . The electricity load and wind power are based on the real operating data of the California Electric Power System CASIO [10] , and the heat load is derived from the Open Power System Data [11] 2017-2018 German users' heat demand.…”

Section: Example Analysismentioning

confidence: 99%

Multi-scenario operation optimization of electric-thermal coupling renewable energy system based on deep reinforcement learning

Wu,

Yang,

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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Within the electric-heat coupling renewable energy system, the complex coupling relationship between electric energy and heat energy, integrated with large-scale of uncertainties in renewable energy and load demand, brings about significant challenges to the real-time economic dispatch of the power system. In view of this, this paper establishes an electric-heat coupling renewable energy optimization model including pumped storage power station and combined heat and power unit which is equipped with heat storage device, and a modified proximal policy optimization (MPPO) algorithms was proposed. First, a vast of electric-heat coupling renewable energy system optimal scheduling problems are expressed as Markov decision processes under the framework of deep reinforcement learning, and then the reward function mechanism is designed to guide the algorithm to generate the best dispatching plan. Next, the collection and sampling method of training samples, as well as the updating mechanism of network parameters and strategies are established. Finally, a case study is carried out with the typical power system, and the simulation results of the electric-heat coupling renewable energy system indicate that the proposed algorithm can effectively reduce the training cost, and the algorithm can handle with varieties of operating situations real-time scheduling of the electric-heat coupling renewable energy system.

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Section: Example Analysismentioning

confidence: 99%