The flexible roles of distributed energy storages in peer-to-peer transactive energy market: A state-of-the-art review

Electricity bills typically account for a large proportion of industrial users' production costs. Hybrid energy storage system (HESS), a high-performance energy storage method, has been widely used on the demand side. In the context of a two-part tariff system, the optimal configuration of battery-ultracapacitor HESS on the industrial load side realizes demand management and cost saving. Complete decoupling of load data promotes the full play to the advantages of batteries and ultracapacitors. Therefore, a VMD-ST-QF load frequency division strategy is proposed to decompose the chronological load curve into high-frequency and low-frequency components precisely. Depending on the results of frequency division, an optimal configuration strategy of HESS is established to minimize the net investment cost of energy storage. In this paper, the economic benefits of assembling energy storage on the industrial load side with four different configuration strategies are compared. The case study shows that the introduction of HESS based on the VMD-ST-QF strategy saves industrial users 5.26% of their total electricity cost and shortens the payback period of energy storage investment. 

Optimal configuration strategy of hybrid energy storage system on industrial load side based on frequency division algorithm

For power plant networks in developing countries like Iraq, balancing electricity demand and generation continues to be a major challenge. Energy management (EM) in either demand-side (DS) or generation-side (GS) strategies, which is frequently utilized in Iraq due to a lack of adequate power generation, has a small impact on the power balancing mechanism. Most previous studies in similar countries discussed only the application of DS strategies. The purpose of this paper is to contrast and review various energy management methodologies being used in developing nations facing power outages, to be able to recommend suitable ones according to the country’s situation. To assess potential EM-based solutions to improve the total energy efficiency of the Iraqi electrical community, a thorough and methodical analysis was carried out. The main objective of this review paper is to discuss the causes of power outages and the energy management strategies addressed here as methods to mitigate or avoid power outages. Unlike existing reviews that concentrated on demand-side energy management, this study specifically focuses on power outage causes in developing countries like Iraq rather than all management strategies. It also introduces the consequences of power outages including analysis of distribution power losses, financial loss from power blackouts, and power blackouts in firms in a typical month. Therefore, it presents readers with state-of-the-art strategies and recommends a generation-based EM strategy to mitigate such issues.

Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies

Optimal configuration and operation for user-side energy storage considering lithium-ion battery degradation

Access to energy storage devices (ESDs) is an effective way to solve the peak traction load shock and Regenerative Braking Energy (RBE) recycling. However, in the real-time operation of the system, there are problems of prediction errors affecting the control results and the problem of energy storage “dead time.” This article analyzes the mechanism of the energy storage “dead-time” problem and aims to solve this problem without increasing the energy storage capacity. Therefore, this article combines the ideas of rolling optimization and predictive control in the model predictive control (MPC) method and proposes an MPC method based on the adaptive correction (AC-MPC) of the State of Charge (SOC) of ESD. The predictive control can ensure the accuracy of power correction; the rolling optimization can reduce the global impact of each correction and reduce the complexity of the calculation. In addition, the rolling prediction in the rolling optimization link reduces the prediction error by reducing the prediction time scale, thus reducing the impact of the prediction error on the control results. The experimental data show that the proposed control strategy can effectively solve the two problems mentioned above, ensure the effect of peak reduction and RBE recovery, and improve the economy of system operation.

Real-Time Control Strategy of Tractive Load Peak Clipping and Valley Filling Based on Model Predictive Control

Distributed energy storage (DES) on the user side has two commercial modes including peak load shaving and demand management as main profit modes to gain profits, and the capital recovery generally takes 8–9 years. In order to further improve the return rate on the investment of distributed energy storage, this paper proposes an optimized economic operation strategy of distributed energy storage with multi-profit mode operation. Considering three profit modes of distributed energy storage including demand management, peak-valley spread arbitrage and participating in demand response, a multi-profit model of distributed energy storage is established, and the proposed optimal operation strategy formulates three stages of the energy storage operation, namely month-ahead, day-ahead, and in-day. In the month-ahead optimization stage, the demand charge threshold of the next month is optimized to minimize the electricity cost. In the day-ahead optimization stage, under the constraint of demand charge threshold and with the goal of maximizing returns, the distributed energy storage is controlled to participate in peak-valley spread arbitrage and demand response, and the optimized output curve for the next day is calculated. In the in-day optimization stage, based on the optimized output curve, taking real-time demand response into account, the real-time charge-discharge power of energy storage is adjusted dynamically with the goal of minimizing income loss, thus to realize adaptive adjustment of distributed energy storage and eliminate the risk of income loss. Simulation results of distributed energy storage for typical industrial large users show that the proposed strategy can effectively improve the economic benefits of energy storage.

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Optimized Economic Operation Strategy for Distributed Energy Storage With Multi-Profit Mode

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Optimized Economic Operation Strategy for Distributed Energy Storage With Multi-Profit Mode