Evolutionary hybrid approaches for generation scheduling in power systems

Unit commitment (UC) is one of the most important optimization tasks of the daily generation scheduling in power system. However, getting the global optimal solution to meet all the constraints is a comparatively tough problem. The binary coding and stochastic operators in traditional genetic algorithm (GA) are not suitable for solving large-scale UC problem. In this paper, the hybrid intelligent messy genetic algorithm (HIMGA) is proposed to solve the problem due to the characteristics of the daily generation scheduling in power system. The proposed algorithm, using operation status of unit as the genotype and combining heuristic self-adapted intelligent operators, is not only very simple but it also reduces the scale of the UC problems, improves the diversity of evolution population, enhances the searching efficiency and develops the convergence of the algorithm. The simulation results prove the correctness and validity of the proposed method.

In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

As an important component of generation scheduling in power system, no matter planning or operation of the Power System, Power load forecasting could be of great significance. In This article, the prediction model integrated the technology of equal dimension and new information and gray prediction model was applied to power load forecasting, and the process of modeling and application were presented through the demonstration, which displayed that the model can provide effective decision support and suit to power load forecasting.

Crisscross PSO algorithm for multi-objective generation scheduling of pumped storage hydrothermal system incorporating solar units

In order to cope with the increasingly complex challenges in power system scheduling, especially in the context of large-scale integration of renewable energy and frequent fluctuations in load demand, this study aims to solve multi-objective optimization problems in power system scheduling, including economy, reliability, and environmental friendliness. First, by introducing a state space model (SSM) to accurately model and predict the dynamic evolution process of the power system in order to grasp the real-time state of the system. On this basis, a multi-objective optimization scheduling model was constructed, which combines various constraints of the power system, adopts an adaptive weight particle swarm optimization (PSO) algorithm for search, and further optimizes the solution using the Cauchy–Lorentz (CL) distribution, aiming to achieve the best balance between economy, environmental protection, and efficiency in power system scheduling and obtain the Pareto optimal solution set. The experimental results indicate that the strategy proposed in this study has significant advantages in improving the operational efficiency of power systems, enhancing system adaptability, and promoting green and low-carbon development. Not only does it provide new theoretical guidance and technical means for the field of power system scheduling, but it also provides strong support for promoting the optimization and transformation of energy structure and achieving sustainable development goals.

Co-operation of electricity and natural gas systems including electric vehicles and variable renewable energy sources based on a continuous-time model approach

A technique for long-term generation scheduling is proposed for power systems with hydro power plants (HPPs). A mathematical formulation of the problem supposes the application of dynamic programming method. The results of yearly generation scheduling in the Electric power system (EPS) of Siberia show strong dependence of the total fuel cost on the optimal monthly electricity production by individual HPPs. The prices of the electricity generated during a year and under different water conditions are calculated for power plants of the EPS of Siberia. The possibility of implementing the long-term optimal generation schedules at power plants by setting the electricity prices is shown.

Distributed Opportunistic Scheduling in Power Systems – An Optimal Stopping Approach

Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective

Generation Scheduling problem by Intelligent Genetic Algorithm

Optimal scheduling of distributed battery storage for enhancing the security and the economics of electric power systems with emission constraints

This paper presents a predictive control approach for long-term generation scheduling of hydro-thermal power systems. The approach is based on an open-loop feedback control scheme that uses a neural fuzzy network forecasting model, for handling the stochastic nature of inflows, and a deterministic nonlinear optimization model, to determine the discharge decisions to be implemented. As a consequence, inflow correlations on time can be represented by nonlinear relationships, and hydropower generation and thermal fuel cost can be handled by nonlinear functions, allowing a more precise modeling of the problem. A simulation model was also developed for performance assessment of the proposed approach. A comparison with the classical stochastic dynamic programming approach, in the case of single reservoir systems, revealed that the latter and the proposed approach perform similarly. The approach was also applied to a multi-reservoir system composed of 19 hydro plants and 10 reservoirs corresponding to a major cascade of the Brazilian power system. The results show that the proposed approach performs as well as in the single reservoir case study.

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method.

With the power system in China had been divided into five major power grid, different stakeholders have been formed, so there is internal competition in the five major power grid. Furthermore, the pollution caused by power industry become seriously. So optimal economic scheduling of electric power system is very important. By optimization, the grid can not only increase revenue by reduce costs, but also can reduce pollution. However, power system optimal dispatch is a complicated and multi-object problem. In this paper, elite group guided quantum-inspired evolutionary algorithm has been adopted. The elite group at each iteration is composed of a certain number of individuals with better fitness values in the current population, all the individuals in the elite group cooperate together to affect quantum-inspired gates to produce off spring. As a weighted state preference can push the genes of individuals to evolve toward state '1'. Simulation results show that the new algorithm is effective.

Uncertain nature of renewable power sources (RES) like wind generation presents a significant issue for system operators. To reduce the negative effects of network congestion on the power system, battery energy storage (BES) Trains offer a potential way to deliver the energy produced by RES to the load center. The effects of stochastic scheduling of BES trains for railway transportation networks with uncertain wind power generation are evaluated in this paper. Using Autoregressive Integrated Moving Average (ARIMA) models, the uncertainties related to wind power for scenario generations are considered. Also, the vehicle routing problem related to the railway transportation system is solved using the time-space network model. As a case study, the BES Train integrated six-bus system with a three-station and three-line railway network is investigated. Simulation results evaluate the impact of BES Train, wind uncertainty, BES Train charging/discharging schedule, wind curtailment and computational time. In addition, BES Train can economically reduce network congestion and decreases operational cost.