• Energy Research

Due to the pressures of energy and environment problems, the dynamic economic emission dispatch (DEED), which is more in line with actual dispatching requirements, has become an important research issue in recent years. In this paper, the highly dimensional, strongly coupled, nonlinear and nonconvex multiobjective DEED model is established considering both the fuel costs and pollution emissions objectives. Furthermore, an improved multiobjective evolutionary algorithm based on decomposition with constraints handling (IMOEA/D‐CH) is proposed to obtain the optimal dispatching schemes. The IMOEA/D‐CH method first decomposes the DEED problem into a number of scalar optimization subproblems and then evolves them simultaneously using the neighborhood information. The real‐time heuristic constraints adjustment and modification methods and the adaptive threshold punishment mechanism are adopted in allusion to the complex constraints of the dispatching model. An evolutionary control strategy is also utilized to avoid excessive evolution of the algorithm toward a certain objective. In addition, the fuzzy decision‐making strategy is used to provide the decision maker with the optimal compromise solutions. To validate the effectiveness of the IMOEA/D‐CH method, the IEEE 30‐bus 6‐generator system and 10‐generator system and 118‐bus 14‐generator system are studied as test cases. The simulation results indicate the validity and superiority of the proposed method compared with the other reported methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The power sector plays a significant role in carbon neutrality strategies, and the grid system is a crucial part of the power sector. In particular, with less mitigation potential from technology efficiency, the credit from whole life cycles is critical. This paper describes the investigation of the environmental impacts of various scenarios from the perspective of life cycles. By using the life cycle assessment (LCA) method, various grid systems are examined as a case study, including a traditional, renewable energy, and power storage grid system, as well as a microgrid, in Guangdong. The results highlight the fact that with the systematic improvement of a grid, significant environmental benefits can be achieved. For a grid system, optimization through technology has significant carbon reduction effects even if the power grid structure is not changed. Using renewable energy instead of traditional fuel can reduce the emission of 0.05 kg of CO2-equivalent greenhouse gas per 1 kWh of electricity produced, which is 7.9% of the baseline, and microgrid technology leads to a much greater carbon reduction potential of 23.8% of the baseline. The role of energy storage is undervalued due to the limitations of the data.

In recent years, the rapid development of electric vehicles has increased the load power system and brought new challenges to the safe and stable operation of the gird. Although the vehicle-to-grid technology can reduce the load that electric vehicles put on the grid, without any incentives, electric vehicle owners are more inclined not to use vehicle-to-grid services. In this paper, therefore, a new dynamic economic emission model based on electric vehicles (DEED_EV) is proposed to maximize the electric vehicle user's revenue, as well as minimize the fuel cost and emission of the thermal power unit. In the DEED_EV model, the stochastic of electric vehicles user's travel and wear of the battery, as well as some constraints such as electric vehicles charging/discharging rate and status, electric vehicles remain power, electric vehicles travel power capacity, ramp limits, up and down reserves, and the system balance are considered. To solve the DEED_EV model, a multi-objective evolutionary algorithm based on decomposition with a step-by-step constraint handling strategy is developed. Different test cases based on the 10-unit are simulated to verify the proposed model and method. The results show that the DEED_EV model not only encourages more electric vehicles to plug into the grid but also reduces the fuel cost and emission of the thermal power unit. Besides, the electric vehicles in the DEED_EV completely realizes the peak shaving and valley filling of the load.

The intermittency of wind power and the large-scale integration of electric vehicles (EVs) bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED) model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G) power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (DEEDIWEv) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEEDIWEv model.

Abstract Electric vehicles (EVs) are currently the most popular green transportation. Wind power has received widespread attention as a kind of renewable energy source. Their rapid development has brought new challenges for the power grid. However, the technology of vehicle to grid (V2G) can realize the peak shaving of the power grid, which is considered to be an effective method to promote the integration of EVs and wind power. Therefore, in this paper, a novel integrated framework of EVs and wind farms (WEV) is proposed to use the EVs charging and discharging to smooth the wind power penalty cost that caused by overestimated and underestimated of available wind power. While a new multi-objective dynamic economic emission dispatching model based on the WEV system (WE_DEED) is developed to consider both emission and total cost objectives. In order to solve the complex constrained WE_DEED problem, we propose a multi-objective differential evolution algorithm with the self-adaptive parameter operator and local search operator based on non-dominant sorting (SaMODE_LS). Moreover, an effective constraint handling method is applied to deal with the constraints in WE_DEED problems. Different test systems based on 10-unit are given to demonstrate the reasonability and efficiency of the proposed model and method.