Energy Storage Aggregator Research Articles

CPS-based power tracking control for distributed energy storage aggregator in demand-side management

The deployment of distributed energy storage on the demand side has significantly enhanced the flexibility of power systems. However, effectively controlling these large-scale and geographically dispersed energy storage devices remains a major challenge in demand-side management. In this paper, we propose a CPS-based framework for controlling a distributed energy storage aggregator (DESA) in demand-side management. Within this framework, a distributed power tracking control algorithm is designed to ensure both power tracking and state-of-charge (SoC) balancing among the energy storage units (ESUs) within the DESA. The proposed algorithm utilizes a distributed observation-based approach that relies solely on local communication. It is demonstrated that the algorithm achieves power tracking convergence within a fixed time, while asymptotically achieving SoC balancing when assuming a connected communication network among the storage units. To validate the theoretical analysis and demonstrate the effectiveness of the proposed control strategy, an example scenario comprising six ESUs is presented.

Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties

The integration of source-grid-load-storage greatly improves the energy managing flexibility of regional integrated energy systems (RIES). Considering the conflicting interests of multi-operator, this paper divides RIES into 1) energy supply center (ESC); 2) load aggregator (LA); 3) energy storage aggregator (ESA), and establishes a multi-operator two-layer optimal scheduling model by combining 1) the Stackelbelberg-Nash asymmetric bargaining game based on the genetic algorithm; and 2) the alternating direction method of multipliers algorithm (GA-ADMM). Firstly, considering the absence of historical data, trapezoidal fuzzy parameters are introduced to construct fuzzy chance constraints based on credibility theory to simulate the source-load uncertainties. Secondly, the uncertainty of demand response behavior, real-time carbon credits trading within RIES, and cooperation-competition behavior of followers (LA and ESA) are considered to simulate the asymmetric relationship between followers and leaders (ESC) based on GA. Then, based on the ADMM algorithm, a Nash asymmetric bargaining game considering energy dependency and energy supply-reception status is proposed to realize the revenue allocation between followers. Finally, simulations are implemented to verify the validity and reliability of the proposed model. Results show that: 1) Cooperation improves the energy-managing flexibility of followers, reduces their dependence on ESC, and thus enhances their game ability. 2) The real-time carbon credits trading enables zero-carbon energy interactions and natural gas replenishment within RIES. 3) Increases in the price of various energy and carbon credits can enhance cooperation between followers.

Battery Cell Balancing of V2G-Equipped Microgrid in the Presence of Energy Storage Aggregator

High electricity consumption in Sarawak’s cities is a concern, driven by residential, industrial, and commercial users. Greater demand for electrical energy requires more electrical resources, affecting grid stability. To prevent blackouts and enhance grid resilience, electric vehicles can serve as distributed energy storage in microgrids. The biggest problem facing energy storage is that not all batteries within energy storage are the same. Every battery cell in the battery pack has a different rate of self-discharge, capacity, internal resistance, and aging, even though they have the same chemical and physical characteristics. These variations lead to power imbalances in energy storage. Therefore, to address this issue, a unique voltage equalization method using a modular flyback converter is presented. Its core objective is to correct voltage imbalances among batteries. The proposed method is modelled using MATLAB, and the performance of the system has been evaluated. Furthermore, the developed model is implemented in a microgrid and tested for two different cases (with and without energy storage for electric cars). From the simulation outcomes, it has been verified that the developed cell balancing technique performs well in equalizing the voltage of cells than the conventional methods.

Game-Theoretic Analysis of Market-Based Operation Mechanism for Energy Storage System

Energy storage system (ESS) is playing an important role in promoting the widespread penetration of renewable energy. However, the contributions of the flexibility provided by ESS are not adequately compensated in the current market mechanisms, which may compromise the enthusiasm for further investing ESS. Focusing on this issue, this article proposes a market-based operation mechanism for ESSs to reasonably evaluate the market value of ESS, as well as motivate them to promote wind power accommodation in base-load periods. Specifically, an ESS trading market is built for the energy storage aggregator (ESA) to purchase charging or discharging power from ESSs, and ESA can compete and bid strategically in the day-ahead (DA) electricity market. In addition, incentive compensation for the ESA in base-load periods is introduced to encourage the ESA to voluntarily and actively participate in wind power accommodation. To examine the effectiveness of the market mechanism, a multi-period joint equilibrium model for the strategic interactions among participants in both the DA electricity market and the ESS trading market is developed. In this model, wind power accommodation needs during base-load periods, as well as technical constraints such as the time coupled charging and discharging power limits of ESS are considered. At last, numerical cases are presented to validate the effectiveness and reasonableness of the proposed mechanism.

Going, Going, Gone: Optimising the bidding strategy for an energy storage aggregator and its value in supporting community energy storage

Energy storage aggregation can bring many advantages to electrical power systems; these include improving flexibility, lowering system costs and reducing the need for carbon-intensive peaking plants. In this work linear and quadratic optimisation models are applied to examine the value derived by an aggregator, using energy storage for arbitrage in Great Britain’s day-ahead and balancing energy markets. The optimum arbitrage strategy is determined when the aggregator is considered a price-taker and a price-maker with access of up to 500 MW battery storage. In the day-ahead market the choice of price-taker or price-maker strategy made negligible difference, however, in the balancing market the price-maker profits were 10.9% higher for 500 MW storage when prices were perfectly forecasted. For imperfect forecasting, the choice of strategy made a negligible difference. A sensitivity analysis challenged the modelling assumptions. System parameters that had the greatest impact on results were the battery efficiency (more efficient, higher profits) and its duration (shorter duration, higher profits). The model was then applied to investigate the potential value that can be obtained by an aggregator with partial access to a community-owned energy storage. Results showed that household electricity bills could be improved by this arrangement. In particular, households that use their batteries less during the winter months, could improve their profits by up to 13.2% if aggregator access is allowed.

Going, Going, Gone: Optimising the Bidding Strategy for an Energy Storage Aggregator and its Value in Supporting Community Energy Storage

Going, Going, Gone: Optimising the Bidding Strategy for an Energy Storage Aggregator and its Value in Supporting Community Energy Storage

Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management

This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi-objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57-bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.

Research on Strategy of distributed energy storage aggregators participating in peak load regulation auxiliary service

In view of the peak shaving problem caused by high proportion of renewable energy connected to the grid, this paper proposes a trading mode in which the distributed energy storage aggregator participates in the peak shaving auxiliary service market, analyzes the operation mode of DSAP participating in peak shaving auxiliary service, and puts forward the optimization model and scheduling strategy of DSAP participating in peak shaving. The feasibility and effectiveness of DSAP participating in peak shaving auxiliary service are verified by numerical simulation.

Discrete specified time consensus control of aggregated energy storage for load frequency regulation

Small scale energy storage systems (ESSs) can serve as buffers for integrating renewable energy resources into the main grid. Their available energy can be aggregated to provide ancillary services for power systems. This paper considers a load frequency control (LFC) scheme with energy storage aggregator comprises of ESSs. To coordinate the behaviors of ESSs for LFC support, a discrete prescribed time consensus control is proposed which ensures that the ESSs can reach consensus within the prescribed settling time. To improve the tracking precision, an optimal leader set is selected with submodular optimization. In the meantime, an event triggered communication (ETC) protocol is proposed to reduce the unnecessary information sharing among ESSs. The ETC protocol is naturally Zeno free with the defined sampling instants. Case studies are conducted to validate the effectiveness of the proposed method, and the performance of the proposed method is superior than conventional and state-of-art methods with comparative studies.

Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and CAES Aggregators

This paper proposes a coordinated strategy of a hybrid power plant (HPP), which includes a wind power aggregator and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday, and balancing markets). The CAES aggregator has an extra ability which is called a simple-cycle mode operation that makes it works like a gas turbine when needed, which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem that can be easily solved by commercially available software such as GAMS. The results that were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit and decrease the financial risks.

Risk-Averse Optimal Bidding of Electric Vehicles and Energy Storage Aggregator in Day-Ahead Frequency Regulation Market

The need for frequency regulation capacity increases as the fraction of renewable energy sources grows in the electricity market. An aggregator can provide frequency regulation by controlling its generation and demand. Here we investigate the participation of an aggregator controlling a fleet of electric vehicles (EVs) and an energy storage (ES) in day-ahead regulation and energy markets and determine the optimal size of the aggregator's bids. The problem is formulated as a stochastic mixed integer linear programming model, taking into account the uncertainties regarding energy and frequency regulation prices. The risks associated with the uncertainties are managed using the conditional value-at-risk method. Because most EVs are charged in residential distribution networks, load flow constraints are also taken into account. A linear formulation based on the rainflow cycle counting algorithm is proposed to include the ES degradation costs incurred from following the frequency regulation signals into the objective function. The problem is studied within the context of a medium-voltage distribution network adopting the market rules of the California Independent System Operator. The results of the numerical analysis show how joint optimization of EVs and ES can improve the aggregator's profit, and verify that the proposed degradation cost formulation can effectively minimize the degradation costs of the ES.

Participation of an Energy Storage Aggregator in Electricity Markets

An important function of aggregators is to enable the participation of small energy storage units in electricity markets. This paper studies two generally overlooked aspects related to aggregators of energy storage: i) the relationship between the aggregator and its constituent storage units and ii) the aggregator's effect on system welfare. Regarding i), we show that short-term outcomes can be Pareto-inefficient: all players could be better-off. In practice, however, aggregators and storage units are likely to engage in long rather than short-term relationships. Using Nash Bargaining Theory, we show that aggregators and storage units are likely to cooperate in the long-term. A rigorous understanding of the aggregator-storage unit relationship is fundamental to model the aggregator's participation in the market. Regarding ii), we first show that a profit-seeking energy storage aggregator is always beneficial to the system when compared to a system without storage, regardless of size or market power the aggregator may have. However, due to market power, a monopolist aggregator may act in a socially suboptimal manner. We propose a pricing scheme designed to mitigate market power abuse by the aggregator. This pricing scheme has several important characteristics: its formulation requires no private information, it incentivizes a rational aggregator to behave in a socially optimal manner, and allows for regulation of the aggregator's profit.

Optimal Scheduling Strategies of Distributed Energy Storage Aggregator in Energy and Reserve Markets Considering Wind Power Uncertainties

With continuous technological improvement and economic development of energy storage, distributed energy storage (DES) will be widely connected to the distribution network. If fragmented DES systems are aggregated to form a distributed energy storage aggregator (DESA), the DESA will have great potential to participate in the day-ahead energy and reserve market and the balancing market. The DESA could act as a mediator between the market and DES consumers, enabling beneficial coordination for DES owners and power systems. This paper presents a bilevel optimization model for DESAs in the energy and reserve market under wind power uncertainties. In the lower-level problem, generating companies, wind power plants (WPP), and DESAs are optimized for scheduling day-ahead (DA) energy and the reserve market. In the upper-level problem, operational strategies for DES systems and DESAs are designed to deal with wind power uncertainties in the balancing market. The DESA splits its resources between the energy and reserve markets so that it can reduce total power system consumption, and mutual profit for the system and end customers is achieved. This model is formulated as a mixed-integer linear programming (MILP) program, which can be solved with commercial software. The validity of the bilevel optimization model is verified by the eight-node test transmission system and IEEE-33 bus distribution system.

Aggregated Energy Storage for Power System Frequency Control: A Finite-Time Consensus Approach

In future power systems, widespread small-scale energy storage systems (ESSs) can be aggregated to provide ancillary services. In this context, a new load frequency control scheme which incorporates the energy storage aggregator (ESA) and its associated disturbance observer is proposed. The disturbance observer is designed to supplement the secondary frequency control for the ESA, therefore the system frequency response and recovery can be improved. Within the ESA, a finite-time leader–follower consensus algorithm is proposed to control the small-scale ESSs via sparse communication networks. This algorithm ensures that the ESA tracks the frequency control signals, while the state-of-charge among each ESS is balanced in finite-time. The external characteristics of the ESA will resemble to that of one large-scale ESS. Numerical examples demonstrate the convergence of the ESA under different communication graphs. The effectiveness of the entire scheme for power system frequency control is validated under a variety of scenarios that include contingency and normal operation.