The value of arbitrage for energy storage: Evidence from European electricity markets

Zafirakis, D.; Chalvatzis, Konstantinos; Baiocchi, Giovanni; Daskalakis, George

doi:10.1016/j.apenergy.2016.05.047

Cited by 129 publications

(59 citation statements)

References 38 publications

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“…As an emerging technology to support the cost-efficient integration of renewable energy, energy storage (ES) has attracted extensive research to investigate its role and value in the future low carbon electricity system. Previous studies analyse the value of ES in US [1], Europe [2] and Australia [3] to perform energy arbitrage by storing low-cost electricity during periods of low net demand and releasing back to the grid during periods of high net demand. While authors in [4,5] demonstrate the increased value of ES by providing both energy arbitrage and ancillary services.…”

Section: Introductionmentioning

confidence: 99%

Business cases for energy storage with multiple service provision

Teng

Štrbac

2016

J. Mod. Power Syst. Clean Energy

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Energy storage (ES) has been considered as the key source of flexibility to support the integration of renewable energy. Previous studies have demonstrated the substantial system cost savings by the deployment of ES, including both investment and operation of generation, transmission and distribution infrastructure. However, this societal benefit may not be realized if industry actors do not have a viable business case to appropriately capture these multiple value streams. In this context, this paper investigates the value that ES may deliver to its owner over two specific business cases in a 2030 UK system. Firstly, the application of large-scale ES in the wholesale market is analysed. It is demonstrated that the optimal allocation of ES to provide multiple services is the key element for ES to become competitive in the electricity market. In the second business case, this paper analyses the value of kilowattscale ES combined with roof photovoltaic (PV) system in the household and community level. The study shows that multiple service provision of ES through advanced pricing schemes, for example time-of-use (ToU) tariff and dynamic distribution use of system (DUoS), lead to higher value and the coordination in the community level could further justify the application of domestic ES.

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Section: Introductionmentioning

confidence: 99%

Business cases for energy storage with multiple service provision

Teng

Štrbac

2016

J. Mod. Power Syst. Clean Energy

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“…In other words, if the constraints are violated in one interval of the net-load scenario, ς gets value 0 independently from the violation or not of the constraints in other intervals, which is guaranteed by (18). The EPSO fitness function comes from the sum of the indicator function for all the considered base net-load scenarios (20). Accordingly, and as defined in (21), for a trajectory to be considered robustly feasible it must comply with a scenario percentage threshold τ , e.g., the number of base net-load scenarios in which the trajectory remains feasible must be greater than a determined percentage of the total number of scenarios.…”

Section: Epso Implementation For the Trajectory Searching Processmentioning

confidence: 99%

Multi-period flexibility forecast for low voltage prosumers

Pinto

Bessa

Matos

2017

Energy

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Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

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“…Some papers have proposed techniques such as integration with other energy sources (solar, fuel cell, hydrogen storage, etc. ), the use of ESRs such as batteries, pumped storage power plant, or compressed air energy storage to overcome the uncertainty in wind power generation . The cooperation of thermal plants and wind power producers is described in Al‐Awami and El‐Sharkawi .…”

Section: Introductionmentioning

confidence: 99%

Optimal energy storage sizing and offering strategy for the presence of wind power plant with energy storage in the electricity market

Aghajani

Kazemzadeh

Ebrahimi

2018

Int Trans Electr Energ Syst

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Summary The current paper aims to simultaneously propose wind power and charge and discharge curve of the battery and analysis effect of the battery on the offering wind power in the different conditions. This paper aims to determine optimal battery specifications to maximize the objective function in a new method, given the impact of battery costs in the objective function. The proposed strategy is based on the production probabilistic scenarios for wind power generation and electricity prices on the day ahead market. Moreover, the penalty factors are used to account for changes to the proposed wind power. Reducing the effective number of scenarios and assign probabilities to each scenario is conducted using self‐organizing feature map neural network in a new method. A 2‐stage stochastic programming method and PSO meta‐heuristic algorithm are used in maximizing the objective function. The simulation results show the effectiveness of the proposed strategy.

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The value of arbitrage for energy storage: Evidence from European electricity markets

Cited by 129 publications

References 38 publications

Business cases for energy storage with multiple service provision

Business cases for energy storage with multiple service provision

Multi-period flexibility forecast for low voltage prosumers

Optimal energy storage sizing and offering strategy for the presence of wind power plant with energy storage in the electricity market

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