Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

Multi-Agent Cooperation Based Reduced-Dimension Q(λ) Learning for Optimal Carbon-Energy Combined-Flow

“…For the purpose of clarifying the internal relation between energy consumption and carbon emissions from power grids, the concept of carbon emission flow is put forward for the first time in reference [33]. On this basis, the authors of [34][35][36] carried out a theoretical analysis and case verification on the carbon emission flow calculation and the carbon flow tracking of a power system, respectively.…”

“…In [23], a distributed multi-step Q(λ) learning was proposed for the complex OPF of a large-scale power system. To satisfy the requirement of multi-objective optimization, an approximate ideal multi-objective solution Q(λ) learning was presented in [36] via a design of multiple Q matrices for different objective functions.…”

“…The carbon-energy combined-flow (CECF) of the power grid is a comprehensive network flow [36], which combines the power flow of the power grid with the carbon emission flow attached to the power flow of the power grid. Among them, the energy flow is the actual network flow, and the carbon emission flow is the virtual network flow, which can be referred to as the carbon flow in the power system.…”

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

“…In 2016, Xu et al [18] presented the preventive-corrective security-constrained optimal power flow (PCSCOPF) to accomplish the best coordination between the preventive control (PC) and corrective control (CC) by considering the probabilistic nature of the contingencies and cost of CC as well as other binding constraints. In 2015, Zhang et al [19] presented the optimal model of carbon energy combined flow (OCECF) which is solved by a new estimated multiobjective solution (AIMS) ( ) learning. The carbon emissions, fuel cost, active power loss, voltage deviation, and carbon emission loss are selected as the optimization objectives.…”

A modified MOEA/D approach to the solution of multi-objective optimal power flow problem

“…The optimal power flow problem as one of the famous multi-objective optimizations also attracts many scholars. Some MOEAs such as multi-objective differential evolutionary [13][14][15][16][17][18], artificial bee colony algorithm [5,[19][20][21][22], multi-objective adaptive immune algorithm [10], enhanced genetic algorithm [23], NSGA-II [7], multi-objective PSO [24,25], quasi-oppositional biogeography-based optimization [26], multi-objective harmony search algorithm [27], modified shuffle frog leaping algorithm [28,29], gravitational search algorithm [1,[30][31][32][33][34], multi-objective modified imperialist competitive algorithm [35,36], multi-hive bee foraging algorithm [37], teaching-learning based optimization algorithm [2,38], multiobjective solution Q() learning [39], etc., have been proposed aiming at the solution of MOOPF problems. However, the above methods have to do some more efforts in order to approach to the true Pareto-Optimal Front and obtain the diversity of the solutions.…”