Demand-side Energy Storage Research Articles

Hierarchical Two-Stage Robust Planning for Demand-Side Energy Storage with Dynamic Carbon Incentive Mechanism

Demand-side energy storage is an important foundation for enhancing load flexibility to accommodate renewable energy. With the widespread adoption of renewable energy, demand-side energy storage planning, and its incentive mechanism have also attracted the attention of a large number of scholars. However, there are still few studies on incentives from a carbon perspective. To fill the gap, a dynamic carbon incentive mechanism is proposed in this study. In addition, a hierarchical two-stage robust planning model for demand-side energy storage that incorporates the proposed carbon incentive mechanism is developed. At the first level, the economic dispatch is performed, and bus carbon intensities are calculated based on the carbon emission flow theory. The second level is a two-stage robust planning model to obtain the optimal capacities of demand-side energy storage, which is solved based on the nested column and constraint generation algorithm. The proposed model is implemented and evaluated on the MATLAB/YALMIP platform using IEEE 24-bus power systems. The results validate the efficacy of the model in promoting carbon-oriented demand-side energy storage planning, leading to a substantial reduction of carbon emissions by 8.44%. Notably, when compared to existing incentive mechanisms, the proposed carbon incentive mechanism exhibits distinct advantages in achieving carbon reduction with less both subsidy costs and fixed investments.

Forecast-Informed Energy Storage Utilization in Local Area Power Systems

Distributed energy storage is beneficial to energy consumers and system operators, but it is expensive to install and requires careful management. To improve the viability of demand-side energy storage, this study presents a new method of obtaining optimal control actions for a demand-side energy storage system in the presence of distributed generation. The optimal usage policy is determined by framing the selection of charge and discharge actions as a stochastic optimization problem, which is conveniently represented as a Markov decision process. The proposed method uses short-term forecasts of loads and local generation to represent the time-dependence and nonstationarity of the net load profile. In contrast to previous studies, the forecast information is used to restrict the problem state space, reducing the computational complexity of the policy calculation. The method is tested using historic load and generation data with real time-of-use rate schedules and achieves substantial reductions in energy costs over similar existing methods.

Feasibility Analysis of Behind-the-Meter Energy Storage System According to Public Policy on an Electricity Charge Discount Program

Energy storage systems are crucial in dealing with challenges from the high-level penetration of renewable energy, which has inherently intermittent characteristics. For this reason, various incentive schemes improving the economic profitability of energy storage systems are underway in many countries with an aim to expand the participation rate. The electricity charge discount program, which was introduced in 2015 in Korea, is one of the policies meant to support the economic feasibility of demand-side energy storage systems. This paper quantitatively evaluated the impact of the electricity charge discount program on the economic feasibility of behind-the-meter energy storage systems. In this work, we first summarized how electricity customers can benefit from behind-the-meter energy storage systems. In addition, we represented details of the structure that make up the electricity charge discount program, i.e., how the electricity charge is discounted through the discount scheme. An optimization problem that establishes a charge and discharge schedule of an energy storage system to minimize each consumer’s electricity expenditure was defined and formulated as well. The case study results indicated that the electricity charge discount program has improved the profitability of behind-the-meter energy storage systems, and this improved profitability led to investment in behind-the-meter energy storage systems in Korea. As a result of the electricity charge discount program, Korea’s domestic demand side energy storage system market size, which was only 27 billion dollars in 2015 in Korea, has grown to 825 billion dollars in 2018.

Peak dispatching for wind power with demand-side energy storage based on a particle swarm optimization model

Electric power system peak dispatching plays a very important role in leveling the load curve, strengthening grid stability, and conserving energy. The inadequate capability of peak dispatching is a leading factor in incomplete wind power integration and poor load characteristics. Adding energy storage on the demand side can improve system peak dispatching ability, promote wind power, and optimize the load curve. This paper first analyzes the mechanisms of regenerative electric heaters (REHs) and electric vehicles (EV) on peak dispatching, based on which a multi-energy hybrid peak dispatching system is designed. Taking into account economic, environmental, and societal benefits and equivalent load curve fluctuation level, this study establishes a multi-energy hybrid peak dispatching optimization model that considers general as well as REH and EV load-control constraints. In view of the shortcomings of particle swarm optimization (PSO), a two-phase force (TPF) rule is introduced in searching process to improve PSO searching performance. The research result reveals that: TPF rule can optimize PSO searching performance and help obtain the best peak-dispatching scheme. In addition, the multi-energy hybrid peak dispatching system designed here offers advantages in leveling the load curve, optimizing power output, conserving energy and reducing emissions, and yield economic benefits.

Provision of Two-area Automatic Generation Control by Demand-side Electric Vehicle Battery Swapping Stations

Application of demand-side resources to automatic generation control (AGC) has a great significance for improving the dynamic control performance of power system frequency regulation. This paper investigates the possibility of providing regulation services by demand-side energy storage in electric vehicle battery swapping stations (BSS). An interaction framework, namely station-to-grid (S2G), is presented to integrate BSS energy storage into power grid for giving benefits to frequency regulation. The BSS can be regarded as a lumped battery energy storage station through S2G framework. A supplementary AGC method using demand-side BSS energy storage is developed considering the vehicle user demand of battery swapping. The effects to the AGC performance are evaluated through simulations by using a two-area interconnected power grid model with step and random load disturbance. The results show that the demand-side BSS can significantly suppress the frequency deviation and tie-line power fluctuations.

Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

Commercial buildings have a significant impact on energy and the environment, being responsible for more than 18% of the annual primary energy consumption in the United States. Analyzing their electrical demand profiles is necessary for the assessment of supply-demand interactions and potential; of particular importance are supply- or demand-side energy storage assets and the value they bring to various stakeholders in the smart grid context. This research developed and applied unsupervised classification of commercial buildings according to their electrical demand profile. A Department of Energy (DOE) database was employed, containing electrical demand profiles representing the United States commercial building stock as detailed in the 2003 Commercial Buildings Consumption Survey (CBECS) and as modeled in the EnergyPlus building energy simulation tool. The essence of the approach was: (1) discrete wavelet transformation of the electrical demand profiles, (2) energy and entropy feature extraction (absolute and relative) from the wavelet levels at definitive time frames, and (3) Bayesian probabilistic hierarchical clustering of the features to classify the buildings in terms of similar patterns of electrical demand. The process yielded a categorized and more manageable set of representative electrical demand profiles, inference of the characteristics influencing supply-demand interactions, and a test bed for quantifying the impact of applying energy storage technologies.

電力市場価格を考慮した需要家側電力貯蔵装置の経済性評価に関する基礎検討

In Japan the electricity market will open on April 1, 2004. Electric utility, Power Producer and Supplier (PPS), and Load Service Entity (LSE) will join the electricity market. LSEs purchase electricity based on the Market Clearing Price (:MCP) from the electricity market. LSEs supply electricity to the customers that contracted with the LSEs on a certain electricity price, and one to the customers that introduced Energy Storage System (:ES) on a time-of-use pricing. It is difficult for LSEs to estimate whether they have any incentive to promote customers to introduce ES or not. This paper evaluates the reduction of LSEs' purchasing cost from the electricity market and other LSEs' purchasing cost by introducing ES to customers. It is clarified which kind of customers has the effect of decreasing LSEs' purchasing cost and how much MCP of the whole power system the demand-side energy storage systems change. Through numerical examples, this paper evaluates the possibility of giving the cost merit to both customers with energy storage systems and LSE by using real data for a year's worth of MCP. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 158(1): 22–35, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20447

将来の需給要因を考慮した我が国における電力貯蔵設備の導入量の評価

The desirable capacity of future energy storage facility in power system in Japan is discussed in this paper, putting emphasis on future new electric demand/supply factors such as CO2 emission problems and social structure change. The two fundamental demand scenarios are considered; one is base case scenario which extrapolates the trend until now and the other is social structure change scenario. The desirable capacity of the energy storage facility is obtained from the result of optimum generation mix which minimizes the yearly expenses of the target year (2030 and 2050). The result shows that the optimum capacity of energy storage facility is about 10 to 15%. The social structure change and demand side energy storage have great influences on the optimum capacity of supply side storage. The former increases storage capacity. The latter reduces it and also contributes to the reduction of generation cost. Suppression of CO2 emission basically affects to reduce the storage capacity. The load following operation of nuclear plant also reduces the optimum storage capacity in the case it produces surplus energy at night. Though there exist many factors which increase or decrease the capacity of energy storage facility, as a whole, it is concluded that the development of new energy storage technology is necessary for future.