Search Results (11)

With the rapid development of new energy vehicle industry and battery technology, in addition to charging mode to supplement energy mode for electric vehicles, battery swapping mode is also about to become an important way for electric vehicles to recharge power. Therefore, in this context, this paper plans the demand response management framework of electric vehicles participating in the regional power grid under the battery swapping mode from the first time. On this basis, the time distribution of battery-swapping demand was proposed by the time series analysis model of different vehicle types of electric vehicles. Then, in order to reduce the peak-valley load difference in the regional power grid as the optimization management goal, the charging schedule optimization scheduling model of electric vehicles participating in the demand response of the regional power grid under the battery swapping mode was constructed. The case analysis shows that under the battery swapping mode, by participating in the demand response through the optimal management and scheduling of the charging load of the power battery, can help the grid balance the contradiction between supply and demand in the peak and valley and promote the full consumption of new energy. Full article

Developing flexible resources is a key strategy for advancing the development of new power systems and addressing the issue of climate change. Demand response is a crucial flexibility resource that is extensively employed due to its sustainability and economy. This work develops a three-stage demand response potential evaluation model based on “theoretical potential–realizable potential–multi-load aggregation potential” in response to the issues of inadequate consideration of numerous complicated agents and time in previous research. Firstly, the traditional method calculates the theoretical maximum demand response potential of a single industry in each period. Based on this, the industry characteristics are taken into account when establishing the demand response potential evaluation model. Lastly, the time variation of the demand response potential is taken into consideration when evaluating the demand response potential of multiple load aggregation. For the analysis, three industries are chosen as examples. The results show that the potential of peak shaving and valley filling obtained by using the model is smaller than that of the traditional method, the reduction range of peak cutting demand response potential calculated by multi-load aggregation is 19–100%, and the reduction range of valley filling demand response potential is 20–89%. The results are closer to reality, which is conducive to improving the accuracy of relevant departments in making relevant decisions and promoting the sustainable development of a new power system. Full article

In order to improve the level of new energy consumption in the system and utilize the clean and efficient characteristics of hydrogen energy, an integrated energy system (IES) scheduling model considering refined utilization of hydrogen energy and generalized energy storage is proposed. Firstly, the two-stage hydrogen energy utilization model of power-to-gas (P2G) is finely modeled, and the waste heat of the P2G methanation reaction is innovatively coupled with the Kalina cycle to improve the thermoelectric decoupling capability of the combined heat and power (CHP) unit. Secondly, integrated demand response, electric vehicles, and hydrogen-containing multi-source energy storage equipment are used as generalized energy storage resources to cut peaks and fill valleys. Then, on the basis of considering the ladder-type carbon trading mechanism, the IES conventional operation model is constructed with the minimum operating cost of the system as the objective function. Furthermore, considering the source-load uncertainty of IES operation, a multi-energy complementary optimal scheduling model of hydrogen-containing IES based on conditional value-at-risk was established. Through simulation analysis, it can be seen that the proposed model takes into account both economic and environmental benefits and improves the system’s ability to “peak cutting and valley filling” and measure risk levels. Full article

In the pilot provinces of China’s electricity spot market, power generation companies usually adopt the separate bidding mode, which leads to a low willingness of demand-side response and poor flexibility in the interaction mechanism between supply and demand. Based on the analysis of the demand response mechanism of the power day-ahead market with the participation of power sales companies, this paper abstracted the game process of the “power grid-sales company-users” tripartite competition in the electricity market environment into a two-layer (purchase layer/sales layer) game model and proposed a master–slave game equilibrium optimization strategy for the day-ahead power market under the two-layer game. The multi-objective multi-universe optimization algorithm was used to find the Pareto optimal solution of the game model, a comprehensive evaluation was constructed, and the optimal strategy of the demand response was determined considering the peak cutting and valley filling quantity of the power grid, the profit of the electricity retailers, the cost of the consumers, and the comfort degree. Examples are given to simulate the day-ahead electricity market participated in by the electricity retailers, analyze and compare the benefits of each market entity participating in the demand response, and verify the effectiveness of the proposed model. Full article

Cermet coatings are post-treated by a new surface microcrystallization technology, namely high-temperature-assisted ultrasonic deep rolling (HT + UDR). The process parameters of ultrasonic deep rolling significantly affect the microstructure and tribological properties of the Ni-WC coatings. In this paper, the samples were treated with different preloading depths (0.20 mm, 0.25 mm, and 0.30 mm), and the microstructure and properties of the coatings were characterized by SEM, EDS, X-ray stress analysis, and micro-Vickers hardness testing. An MMW-1A-type friction and wear tester was used for the dry friction and wear test at room temperature, respectively. Compared with the untreated sample, plastic rheology occurred on the surface of the coatings after HT + UDR, showing a phenomenon of “cutting peaks and filling valleys”. In the treated coatings, visible cracks were eliminated, and the inside of the coating was denser. The surface hard phase was increased as a “skeleton” and embedded with the soft phase, which played a role in strong and tough bonding. After HT + UDR + 0.25 mm treatment, the surface roughness increased by 68%, the microhardness of the surface layer reached a maximum of 726.3 HV_0.1, and the residual tensile stress changed from 165.5 MPa to −337.9 MPa, which inhibited the germination and propagation of cracks. HT + UDR improved the wear resistance of the coating in many aspects. The coating after the 0.25 mm preloading depth treatment possessed the smallest friction coefficient and the lowest wear amount, which is 0.04 and 4.5 mg, respectively. The wear form was abrasive wear, and the comprehensive tribological performance is the best. Full article

With the development of energy integration technology, demand response (DR) has gradually evolved into integrated demand response (IDR). In this study, for the integrated energy system (IES) on the distribution grid side with electricity, heat, natural gas network, and hydrogen energy equipment, the analogy relationship between the thermal and mobile hydrogen energy storage networks is proposed. Moreover, a unified model that reflects network commonalities across different energy forms is established. Then, considering the time delay of the IES in the nontransient network, a time-domain two-port model of the IES considering the time delay is established. This model shows the joint effect of time and space on system parameters. Finally, this study validates the model in the application of DR. The verification results show that in DR, the time-domain two-port model can accurately “cut peaks and fill valleys” for the IES and effectively reduce the operating cost of the IES system. Full article

The large net torque fluctuations in the reducer output shafts of conventional beam pumping units and the existence of negative torque are the decisive factors that lead to their low efficiency and high energy consumption. This study developed a positive torque modulation scheme for conventional beam pumping units, which was based on the principle of the follow-up secondary balance of the connecting rod. The CYJ10-4.2-53HF conventional beam pumping unit was selected as the research object. The kinematic and dynamic simulation analysis of the modified pumping unit was carried out using ADAMS software. The results showed that secondary balance torque curves could realize the function of “peak cutting and valley filling” for the curves after the primary balance and that the modified pumping unit could achieve a full-cycle positive value for the reducer output shaft and verify the feasibility of our modulation scheme. A secondary balance offset angle of 315° was the best choice as the amplitude of the torque curve clearly increased and the phase remained basically the same when the radius of the mass center of the secondary balance increased. Therefore, when the offset angle value of the secondary balance weight was determined, the radius of the mass center could be changed by adjusting the position of the secondary balance weight to achieve the balance adjustment. Full article

The premise of the large-scale operation of electric buses corresponds to efficient charging service guarantees. Recent research on charging stations mainly aims to obtain the construction location and construction sequence through optimization methods or decision-making methods. This research has considered the aspects of geography, charging efficiency, economic efficiency, and emergency response capacity. The increase of charging stations will lead to competition among charging stations, unbalanced use of charging facilities, and unnecessary loss of electricity to the power grid. In fact, few studies pay attention to the actual operation of existing charging stations. Therefore, it is necessary to establish a scientific, comprehensive, and efficient charging services evaluation framework to support the actual operation of charging stations. Based on the analytic hierarchy process (AHP), this paper designs a multi-level indicator evaluation framework, which includes 6 first-level indicators and 20 s-level indicators. The first-level indicators are cutting peak and filling valley (A1), location and scale (A2), intelligent technology (A3), equipment efficiency (A4), operating income (A5), and reliability (A6). Through the questionnaire survey of ten experts in related fields, we understood the importance and attention of these indicators. The results show that the weights of indicators of location and scale index (A2) and reliability (A6) are high, which are 0.2875 and 0.2957, respectively. The least concerned indicator is equipment utilization efficiency (A4), at a weight of 0.0531. According to the actual data of charging stations in Zhengzhou, China, the comprehensive competitiveness of several charging stations is evaluated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The result shows that station 1 has the highest comprehensive competitiveness, followed by station 2 and station 7. The evaluation framework proposed in this paper comprehensively considers a variety of factors. The combination of AHP and TOPSIS can reduce the uncertainty in experts’ evaluation of the service of the charging station. Full article

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage. Full article

With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market. Full article

To promote coordinated development of electric vehicles (EVs) and power grid under open power selling, a bidding mechanism using blockchain smart contract technology was proposed. By demand respone management (DRM) on and off the blockchain, based on different driving characteristics of EV subgroups, various charging–discharging demands and constraints were fully considered between EV user subgroups and agent. Purchase–sale transaction relationship and unit commitment plan were fully considered between the EV agent and power dispatching center under economic dispatching. Aiming at the lowest power purchase cost of EV users, the highest profit of EV agent and the lowest cost of power economic dispatching, smart contract models with optimal benefits were established among the three. The smart contract models were solved by combining the internal and external optimization relationship of particle swarm and genetic algorithms. The charging–discharging price was optimized by DRM to realize the reasonable allocation of charging–discharging resources of EVs. An example analysis shows that this bidding mechanism can achieve peak–cutting and valley–filling for power load. At the same time, it can effectively protect the benefits of EV users, agent, and power dispatching center. This result can provide a reference for the application of smart contract in bidding of EVs to the power grid. Full article