Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

This article proposes a simple and reliable Dynamic Economic Emission Dispatch (DEED) model for a power system integrating Renewable Energy Sources (RES), including photovoltaic (PV) systems and wind energy. The thermal units in the system under concern are operating under limited fuel constraints. The proposed DEED effectively manages limited fuel conditions through Dynamic Generation Capacity (DGC). The Walrus Optimization Algorithm (WaOA) and Marine Predators Algorithm (MPA) are competing for the implementation of the proposed DEED with DGC and RES. The performance of the competing optimizers is compared against a mature meta-heuristic optimizer, Particle Swarm Optimization (PSO). The MPA is the most promising candidate, significantly outperforming the contenders, WaOA and PSO. For the 1000MW load scenario, MPA and WaOA achieved a reduction in generation cost compared to PSO. Moreover, MPA substantially reduced the variability of results, demonstrating superior consistency compared to the competing optimizers. The proposed energy management strategies are tested on a system with 10 units under various operating scenarios. The results showed that the RES for power systems with limited/shortage fuel scenarios were insufficient to meet the required load, as fuel shortages tend to occur suddenly and unpredictably, thereby reducing system security. The DEED approach, incorporating both DGC and RES, achieved a notable reduction in fuel costs and emissions compared to the full fuel scenario and the fuel shortage scenario without RES. The results highlight the potential of integrating both DGC and RES into the DEED framework, demonstrating effectiveness in reducing operational costs and emissions while simultaneously enhancing system security and contributing to the development of a sustainable, intelligent future power grid.

This paper deals with dynamic combined economic emission dispatch problem of an electric power system consisting of thermal, hydro and photovoltaic (PV) power plants. Mathematical models of these plants as well as the model of dynamic combined economic-environmental nonlinear optimization problem are given. The proposed optimization model was implemented in publicly available GAMS software, and the result is the optimal load distribution among the plants during a one-day optimization period. The software is applied to the power system without PV power plants and with a number of new PV plants added to the existing hydrothermal system. The results quantify the positive effects of PV power plants on both fuel cost and greenhouse gas reduction and recommendations are given related to the application of PV generation in modern systems. Software efficiency in solving both dynamic economic dispatch (DED) and dynamic combined economic emission dispatch (DCEED) problems is discussed, and the conclusions are drawn about its application possibilities.

A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management

Increasing environmental concerns have led to a push for renewable energy sources to be extensively used to reduce emissions. In this paper, we investigate a hybrid dynamic economic emission dispatch (HDEED) problem involving thermal, wind, and photovoltaic (PV) generation systems. Our formulation considers the stochastic nature of both wind and PV generated power, and differences because of mismatches between the actual and allocated wind and PV power. A hybrid backtracking search algorithm with sequential quadratic programming was used to minimize the total operational costs and emissions, while dispatching power to the committed generation units subject to all operational constraints. To verify the efficacy, we applied the proposed technique to solve the dynamic economic emission dispatch problem on five and ten unit test systems. The proposed technique was also used to solve the HDEED problem on IEEE 30 bus, 6-unit and IEEE 57 bus, 7-unit test systems, with and without renewable generation. The results of our numerical simulations show the efficiency of the proposed technique with respect to reducing operational costs and emissions. Moreover, our results show that by incorporating renewable energy into existing power systems, we can reduce operational costs and emissions.

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

This study evaluated the energy saving potential of renewable energy generation systems based on integrated solar energy in an urban environment. The solar city concept was implemented using photovoltaic (PV) and solar thermal systems. As a case study, the Sejong national pilot smart city in South Korea was selected to evaluate the renewable energy penetration rate. For evaluating the proposed renewable energy systems, the electrical and thermal loads of the smart city were estimated using field measurement data. Then, the renewable energy penetration rate of the city was evaluated. The HomerPro software was used to analyze the PV generation and operating energy consumption of the natural gas (NG) generator with a district heating network. The thermal load-supporting potential of the solar thermal system was estimated using the TRNSYS software. The results showed that the proposed urban integrated renewable energy system could meet over 30% of the renewable energy penetration rate and the levelized cost of energy and total net present cost was 7% lower than the base case system (i.e., NG generator). The proposed system also exhibited 38% less CO2 emissions than the base case system.

In association with the development of intermittent renewable energy generation (REG), dynamic multiobjective dispatch faces more challenges for power system operation due to significant REG uncertainty. To tackle the problems, a day‐ahead, optimal dispatch problem incorporating energy storage (ES) is formulated and solved based on a robust multiobjective optimization method. In the proposed model, dynamic multistage ES and generator dispatch patterns are optimized to reduce the cost and emissions. Specifically, strong constraints of the charging/discharging behaviors of the ES in the space‐time domain are considered to prolong its lifetime. Additionally, an adaptive robust model based on minimax multiobjective optimization is formulated to find optimal dispatch solutions adapted to uncertain REG changes. Moreover, an effective optimization algorithm, namely, the hybrid multiobjective Particle Swarm Optimization and Teaching Learning Based Optimization (PSO‐TLBO), is employed to seek an optimal Pareto front of the proposed dispatch model. This approach has been tested on power system integrated with wind power and ES. Numerical results reveal that the robust multiobjective dispatch model successfully meets the demands of obtaining solutions when wind power uncertainty is considered. Meanwhile, the comparison results demonstrate the competitive performance of the PSO‐TLBO method in solving the proposed dispatch problems.

High penetration of renewable energy in the future power system will pose a big problem to the load dispatch operation. The large disturbance and high forecast error must be considered when scheduling a limited number of controllable generators to follow rapid change in load. This paper proposes a dynamic economic load dispatch (DELD) problem approach based on the concept of a feasible operation region (FOR). FOR is defined as the region that committed generators may operate in to match the load profile without violating the ramp‐rate constraints. The DELD problem is solved in two stages. In the first stage, FOR of each generator is computed using recent real‐time forecasted load as well as renewable energy generation. In the second stage, a generation schedule is determined by solving the DELD problem interval by interval while considering ramp‐rate constraints and FOR constraints. The method can gives feasible solution for feasible load and specify the amount of compensation required for feasible solution for infeasible load. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature.

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

Power distribution costs are no longer just a utility's responsibility. Instead, in order to fulfil the soaring demand for power, the utilities place a strong emphasis on reducing the amount of harmful chemicals that are released into the environment. In this study, the distribution system for 15 units is subjected to dynamic economic emission dispatch (DEED) both with and without demand side control (DSM). Every hour, the elastic loads are ideally scheduled by DSM and moved to times when the price of power is lowest. This serves to lower the system's peak demand, lower the cost of its generation, and raise its load factor. For the subject distribution test system, DEED is carried out using the price-penalty factor (PPF) technique and fractional programming (FP) approach, and a comparison between the two is based on which method emits the fewest hazardous and poisonous chemicals into the environment.

Renewable energy generation has different static and transient characteristics than traditional synchronous power plants. The connection of renewable energy to the grid will have varying degrees of impact on the voltage stability of the local power grid. In a power grid with large renewable energy penetration, in addition to the voltage stability problem, renewable energy integration changes the original power flow distribution, line transmission power and inertia of the entire system, the power system transient stability has undergone tremendous changes. Renewable energy power generation technology and grid-connected scale are developing rapidly. The technical requirements for renewable energy and the research on grid-connected technology need to be continuously deepened and improved. However, some large-scale renewable energy off-grid accidents in recent years have brought serious threats to the security and stability of power systems, and have also raised higher research requirements for the stability of large-scale renewable energy grid-connected. This paper provides an introduction about the control and stability of renewable energy grid integration on both technologies and standardization aspects, and tries to explore potential the trend in active power and frequency control, reactive power and voltage control, security and stability control respectively caused by the integration of RE generation into power systems.

Unveiling the dynamic impact of energy generation on economic sustainability in Canada: A roadmap towards sustainable development

Towards renewable energy generation and low greenhouse gas emission in high-income countries: Performance of financial development and governance