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Energy hubs play an important role in implementing the integrated energy system as an interconnection point between various energy components and networks. Energy networks also influence the management of the energy hubs. Therefore, the problem of optimal operation of the energy hubs and energy networks is modeled as a bi-level optimization problem in this paper. In the proposed bi-level model, the...
This paper proposes a Quasi-Monte Carlo (QMC) simulation based multi-objective economic dispatch, which aims to reduce the fuel cost and emission of the grid simultaneously. During the simulation, QMC models the stochastic behaviours of wind speed and distributed loads with low-discrepancy sequences. In comparison with conventional Monte Carlo (MC) simulation, the computational complexity of QMC is...
This paper aims to solve the optimal power and gas flow problem of the integrated electricity and natural gas networks. Gas-fired power plants provide linkage between electricity and natural gas networks. The model of natural gas network consisting of gas sources, loads, pipelines and compressors is calculated by the Newton-Raphson method. Afterwards, a multi-objective group search optimizer with...
This paper presents a probabilistic interval optimization (PIO) model for evaluating the problem of optimal power flow considering wind power integrated (OPFWP). In PIO model, the wind power is deemed as a probability interval variable to assess its profit and risk simultaneously. To be precise, the profit is manifested by the net decrease of generation cost between the same power system with and...
This paper proposes a power conversion system for a treadmill with auto-transferring modes between a motor and a generator. With the grid-connection function, the kinetic energy of the treadmill can be converted into electric power and fed into the grid. An infrared sensor is equipped in the control loop to detect the position of the user on the treadmill. Based on the position of the user, the reaction...
This paper presents an evolutionary algorithm, reference point based multi-objective group search optimizer using non-dominated sorting approach (r-NSGSO), for optimal power flow problem with multiple objectives. First, the six objectives of optimal power flow are reduced to two objectives which represent the secure and economic indices, respectively. The r-NSGSO integrates the non-dominated sorting...
This paper focuses on implementing a dimensional Q-learning (DQL) for solving reactive power optimization with discrete control variables. The proposed algorithm applies the traditional Q-learning to search the feasible region dimensionally, so that the memory amount of each agent can be largely reduced. Meanwhile, the safety margin of voltage amplitude and reactive power output of generators are...
This paper proposes a multi-objective optimization method for solving the Security-Constrained Optimal Power Flow (SCOPF) problem with the consideration of solar farm integrated and distributed load variations in the grid. In this scheme, the power generated by solar farm is affected by the weather uncertainty. The dispatch objectives are formulated to minimize fuel cost and emission simultaneously...
To improve power system stability in this paper, a method for optimal coordinate control of power system stabilizers (PSSs) and static synchronous compensator (STATCOM) is presented. The optimal coordinated control of multiple PSSs and STATCOM is transformed into an optimization problem in which both rotor angle speed deviation between generators and load voltages deviation after fault are involved...
This paper proposes a probabilistic model for optimal joint allocation of energy and spinning reserve to determine energy and spinning reserve capacities. The model takes into consideration both the hourly changes of load demands and the probability of generators' contingencies. The objective function aims at minimizing not only fuel costs caused by power generation but also the costs associated with...
This paper proposes a model of a central heating electric boiler integrated with a stand-alone wind generator. The paper also investigates control schemes for the wind turbine generator and the electric boiler. For the wind turbine generator, the two control schemes are proposed for control of the pitch angle and generator rotor speed respectively. The pitch angle control uses the generator output...
Pollution of the power plant has caused harmful environmental effects due to the emission of greenhouse gas. The pollution can be reduced by adjusting the real power outputs of different power plants. However, the relocation of real power outputs results an additional outlay in the system. In order to eliminate the conflict between the cost and emission, an optimal power flow is introduced in this...
This paper presents a new model for the integrated maintenance scheduling (IMS) of generators and transmission lines, which is formulated as a high dimensional, mix-integer and highly constrained optimization problem. The advantage of the new model is that the number of integer variables is greatly reduced in comparison with that in the traditional IMS model in a large scale power system. Besides,...
This paper presents Function Optimization by Learning Automata (FOLA) for the power flow problem which aims to achieve economic power system dispatch and voltage stability enhancement in dynamic wind power integrated systems. Dividing each dimension into a certain number of cells, FOLA undertakes the dimensional search, and has the ability of memorizing history through the values of cells that have...
This paper presents a new method called Multi-objective Optimization by Reinforcement Learning (MORL), to solve the optimal power system dispatch and voltage stability problem. In MORL, the search is undertaken on individual dimension in a high-dimensional space via a path selected by an estimated path value which represents the potential of finding a better solution. MORL is compared with multi-objective...
This paper presents a new algorithm, Function Optimisation by Reinforcement Learning (FORL), to solve large-scale and complex function optimisation problems, in particular for those in a high-dimensional space. FORL undertakes the dimensional search in sequence, in contrast to evolutionary algorithms (EAs) which are based on the population-based search, and has the ability of memory of history incorporated...
Optimal Power Flow (OPF) is an effective tool for dispatch planning of power systems, and the algorithms aiming to solve the OPF have been widely studied. Among these algorithms, the Evolutionary Algorithms (EAs) are a popular type, which have been investigated. However, EAs are also notorious for the intensive computation caused by a large amount of evaluations of the objective function required...
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