Search Results (8)

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids. Full article

With the increasing liberalization of energy markets, the penetration of renewable clean energy sources, such as photovoltaics and wind power, has gradually increased, providing more sustainable energy solutions for energy-intensive industrial sectors or parks, such as iron and steel production. However, the issues of the intermittency and volatility of renewable energy have become increasingly evident in practical applications, and the economic performance and operational efficiency of localized microgrid systems also demand thorough consideration, posing significant challenges to the decision and management of power system operation. A smart microgrid can effectively enhance the flexibility, reliability, and resilience of the grid, through the frequent interaction of generation–grid–load. Therefore, this paper will provide a comprehensive summary of existing knowledge and a review of the research progress on the methodologies and strategies of modeling technologies for intelligent power systems integrating renewable energy in industrial production. Full article

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration. Full article

To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensitive to load changes. The solution iteration of the cuckoo algorithm is 18 times. Meanwhile, the system requires four updates of capacity allocation results for 20 years of operation. This result validates the effectiveness of the proposed model and methodology. It also provides a reference for the research and construction of capacity allocation of microgrid systems at the park level. Full article

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids. Full article

Under a two-part tariff, the user-side installation of photovoltaic and energy storage systems can simultaneously lower the electricity charge and demand charge. How to plan the energy storage capacity and location against the backdrop of a fully installed photovoltaic system is a critical element in determining the economic benefits of users. In view of this, we propose an optimal configuration of user-side energy storage for a multi-transformer-integrated industrial park microgrid. First, the objective function of user-side energy storage planning is built with the income and cost of energy storage in the whole life cycle as the core elements. This is conducted by taking into consideration the time-of-use electricity price, demand price, on-grid electricity price, and energy storage operation and maintenance costs. Then, considering the load characteristics and bidirectional energy interaction of different nodes, a user-side decentralized energy storage configuration model is developed for a multi-transformer-integrated industrial park microgrid. Finally, combined with the engineering practice constraints, the configuration model is solved by mixed integer linear programming. The simulation test demonstrates how the proposed model can successfully increase the economic benefits of an industrial park. Electricity and demand costs are reduced by 11.90% and 19.35%, respectively, and the photovoltaic accommodation level is increased by 4.2%, compared to those without the installation of energy storage system. Full article

The Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks. Full article

To make full use of distributed energy resources to meet load demand, this study aggregated wind power plants (WPPs), photovoltaic power generation (PV), small hydropower stations (SHSs), energy storage systems (ESSs), conventional gas turbines (CGTs) and incentive-based demand responses (IBDRs) into a virtual power plant (VPP) with price-based demand response (PBDR). Firstly, a basic scheduling model for the VPP was proposed in this study with the objective of the maximum operation revenue. Secondly, a risk aversion model for the VPP was constructed based on the conditional value at risk (CVaR) method and robust optimization theory considering the operating risk from WPP and PV. Thirdly, a solution methodology was constructed and three cases were considered for comparative analyses. Finally, an independent micro-grid on an industrial park in East China was utilized for an example analysis. The results show the following: (1) the proposed risk aversion scheduling model could cope with the uncertainty risk via a reasonable confidence degree β and robust coefficient Γ. When Γ ≤ 0.85 or Γ ≥ 0.95, a small uncertainty brought great risk, indicating that the risk attitude of the decision maker will affect the scheduling scheme of the VPP, and the decision maker belongs to the risk extreme aversion type. When Γ $\in$ (0.85, 0.95), the decision-making scheme was in a stable state, the growth of β lead to the increase of CVaR, but the magnitude was not large. When the prediction error e was higher, the value of CVaR increased more when Γ increased by the same magnitude, which indicates that a lower prediction accuracy will amplify the uncertainty risk. (2) when the capacity ratio of (WPP, PV): ESS was higher than 1.5:1 and the peak-to-valley price gap was higher than 3:1, the values of revenue, VaR, and CVaR changed slower, indicating that both ESS and PBDR can improve the operating revenue, but the capacity scale of ESS and the peak-valley price gap need to be set properly, considering both economic benefits and operating risks. Therefore, the proposed risk aversion model could maximize the utilization of clean energy to obtain higher economic benefits while rationally controlling risks and provide reliable decision support for developing optimal operation plans for the VPP. Full article