The power-to-gas (P2G) process, whereby excess renewable electrical energy is used to form hydrogen and/or synthetic natural gas (NG) that are injected, transported, and stored in the gas network, has the prospect to become an important flexibility option for the seasonal storage of low-carbon electricity. This study is the first to model and assess the potential of P2G when combined with gas seasonal storage operation accounting for the two networks’ characteristics and constraints (including the amount of hydrogen that can be blended with NG under different gas network conditions). Power system operation with P2G is analysed via a two-stage optimisation based on DC power flow to assess the gas production from otherwise curtailed renewables, also considering impact of P2G on short-term and long-term gas prices. Additionally, impact of P2G on gas network operation and its potentially required re-dispatch are evaluated with a steady-state gas flow model. Case studies conducted on the Great Britain gas and electrical transmission networks quantify benefits and limitations of the integrated usage of P2G with seasonal gas storage under different scenarios. The proposed model thus sets the fundamentals for further development of this emerging technology as a seasonal storage option in low-carbon power systems.

This study presents a mathematical model of lithium-ion (Li-ion) batteries in the energy management (EM) problem of a microgrid (MG). In this study, the authors develop a detailed model of Li-ion batteries that considers the degradation cost associated with operation, controllable and uncontrollable charging ramps, other limits, and the operating characteristics provided by the manufactures. The Li-ion battery degradation cost is analysed using different approaches and is compared with modelling without this cost, using a quadratic degradation cost, and using a piecewise degradation cost. Furthermore, this cost is analysed using a linear cost that takes the life expectancy based on the number of cycles of the battery into account. To analyse the proposed method and other modelling approaches, the authors examine the battery model in an EM problem in an MG. This MG, which can be connected to the main grid, also uses wind and photovoltaic as generation resources, in addition to a backup generator. The EM problem is modelled as a deterministic mixed-integer linear (or quadratic) problem; the results of eleven different cases are used in the analysis of the proposed Li-ion battery model for a 24 h planning horizon.

Collectively, thermostatically controlled loads (TCLs) offer significant potential for short-term demand response. This intrinsic flexibility can be used to provide various ancillary services or to carry out energy arbitrage. This study introduces an aggregate description of the flexibility of a heterogeneous TCL as a leaky storage unit, with associated constraints that are derived from the TCL device parameters and quality of service requirements. In association with a suitable TCL control strategy this enables a straightforward embedding of TCL dynamics in optimisation frameworks. The tools developed are applied to the problem of determining an optimal multi-service portfolio for TCLs. A linear optimisation model is constructed for the optimal simultaneous allocation of frequency services and energy arbitrage. In a case study, optimal service allocations are computed for eight representative classes of cold appliances and the results are validated using simulations of individual refrigerators. Finally, it is demonstrated that clustering of appliances with similar capabilities can significantly enhance the flexibility available to the system.

Storage technology is a key enabler for the integration of renewable energy resources into power systems because it provides the required flexibility to balance, the net load variability and forms a buffer for uncertainties. A solution for sizing of energy storage devices in electric power systems is presented. The considered planning problem is divided into two time perspectives: hourly and intra-hour intervals. For the intra-hour time horizon, the algorithm determines the optimal size of the energy storage devices to provide the adequate ramping capability for the system. This ramping capability guarantees the system ability to follow the load in the intra-hour intervals, as well as to alleviate short-term wind generation and load fluctuations. In the hourly time scale, the optimal size of the storage is determined with respect to having a sufficient generation capacity to support the loads. A 6-bus test power system is studied to show the effectiveness of the proposed algorithm.

The presence of distributed generation (DG), represented by photovoltaic generation and wind generation, brings new challenges to distribution network operation. To accommodate the integration of DG, this study proposes a bi-level optimisation model to determine the optimal installation site and the optimal capacity of battery energy storage system (BESS) in distribution network. The outer optimisation determines the optimal site and capacity of BESS aiming at minimising total net present value (NPV) of the distribution network within the project life cycle. Then optimal power flow (OPF) and BESS capacity adjustment are implemented in the inner optimisation. OPF optimises the scheduling of BESS and network losses. On the basis of optimal scheduling of BESS, a novel capacity adjustment method is further proposed to achieve the optimal BESS capacity considering battery lifetime for minimising the NPV of BESS. Finally, the proposed method is performed on a modified IEEE 33-bus system and proven to be more effective comparing with an existing method without BESS capacity adjustment.

This study focuses on the design issue of battery energy storage system (BESS) for a wind–diesel off-grid power system located in the Whapmagoostui community in Quebec, Canada. The local range of wind speed is from 0 to 24.8417 m/s, and the total yearly load demand in 2013 was 11,176 MWh. An optimal planning model is proposed in this study with the objectives of maximising the economic, environmental benefits, and reliability of the system. The battery energy capacity and the rated capacity of converter are selected as the optimal variables. In order to consider the impacts of renewable energy randomness, the uncertainty of component failures, and the power flow constraints on planning results, quasi-steady state simulation is adopted to calculate the indices for each design scheme of BESS. The proposed optimal planning model of BESS is implemented and verified in the Whapmagoostui community. Also, a detailed analysis of several scenarios is presented. A base scenario with three diesel generators and four wind turbines is investigated, and its optimal BESS integration reduces fuel consumption by 4% and improves the average annual profit by 19%. The optimal designing of BESS enhances the economic, environmental benefits, and reliability of the wind–diesel system with high fuel prices in the Whapmagoostui community.

Optimal performance of power distribution networks greatly depends on network configuration, location and size of distributed generations (DGs) units and storage systems. That is, for different configurations, different optimal locations and sizes of DGs can be found and vice versa. Therefore, the impact of both location and size of DG and network configuration should be considered in the planning process, simultaneously. Also, the presence of storage systems in the distribution system leads to some loads to be supplied even in fault conditions. In this study, distribution system reconfiguration (DSR), for considering network configuration effect that runs in offline mode with constant loads, and optimal DG allocation and sizing problems are studied simultaneously to find an optimal condition for distribution network based on operational thresholds and reliability improvements. Non-dominated Sorting Genetic Algorithm is used to solve these problems simultaneously. Power losses, energy not supplied (ENS) and the costs associated with DG are the objectives that are studied. The method of calculating ENS in DSR problem in the presence of DGs with storage systems is explained and the impact of protective equipment is considered, as well. The proposed approach is applied on different test systems, and its effectiveness is shown in various conditions.

Nowadays, optimal operational planning of micro-grid (MG) with regard to energy costs minimisation of MG and better utilisation of renewable energy sources (RES) such as solar and wind energy systems, has become the head of concern of modern power grids and energy management systems. Due to large integration of RES into the MG, the necessity of battery energy storage (BES) has increased rapidly. Size of BES plays an important role in the operation cost minimisation of MG. A cost-based formulation has been performed in this study to determine the optimal size of BES in the operation cost minimisation problem of MG under various constraints, such as power capacity of distributed generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction. A recently developed optimisation technique known as grey wolf optimisation (GWO) has been applied here to solve the problem. The proposed algorithm is tested on a typical MG. Simulation results establish that the proposed approach outperforms several existing optimisation techniques such as genetic algorithm, particle swarm optimisation, tabu search, differential evolution, biogeography-based optimisation, teaching–learning-based optimisation, bat algorithm (BA) and improved BA in terms of quality of solution obtained and computational efficiency.

This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, demand side management, on-load tap changer tap adjustment and reactive power compensation are considered. The optimisation of DESS for peak shaving and operation cost decreasing is also integrated. The expansion model allows alternatives to be considered for new wiring, new substation, substation expansion and DG installation. The distribution network expansion planning (DNEP) problem is a mixed integer non-linear programming problem. Active management and uncertainties especially with the DG integration make the DNEP problem much complex. To find the suitable algorithm, this study converts the DNEP problem to a second-order cone programming model through distflow equations and constraints relaxation. A modified 50-bus application example is used to verify the proposed model.

Changes in the electricity business environment, dictated mostly by the increasing integration of renewable energy sources characterised by variable and uncertain generation, create new challenges especially in the liberalised market environment. The role of energy storage systems (ESS) is recognised as a mean to provide additional system security, reliability and flexibility to respond to changes that are still difficult to accurately forecast. However, there are still open questions about benefits these units bring to the generation side, system operators and the consumers. This study provides a comprehensive overview of the current research on ESS allocation (ESS sizing and siting), giving a unique insight into issues and challenges of integrating ESS into distribution networks and thus giving framework guidelines for future ESS research.

Weak low voltage ride-through (LVRT) ability and unstable output power are two major problems faced by the doubly-fed induction generator (DFIG). To solve these two problems simultaneously, a commercially available fault current limiter-battery energy storage system (FCL–BESS), which is suitable to be applied in a microgrid, is proposed in this study. During normal operation, the FCL–BESS stabilises the output power of DFIG by compensating the fluctuating component of DFIG output power with energy buffering capability provided by the battery energy storage system (BESS). On occurrence of a grid fault, the FCL–BESS enhances the LVRT ability of DFIG by inserting the fault current limiting inductor into the stator, which weakens the rotor back electromagnetic force voltage and limits the rotor overcurrent. The FCL–BESS also stabilises the DC-link voltage by the BESS, which further strengthens the controllability of grid-side converter and rotor-side converter. Moreover, the FCL–BESS is able to help grid voltage recover fast and smoothly by providing power support to the grid. Experiments under different conditions have been carried out with a 2 kW prototype to evaluate the performance of the proposed FCL–BESS. Experimental results show that the FCL–BESS have much better performance than using single fault current limiter or BESS and can solve the two problems faced by the DFIG simultaneously and effectively.

With integration of a battery energy storage system (BESS) into the wind power system, the wind power variation can be mitigated to dispatch a constant power to the grid. To effectively supply the constant power dispatch to the grid with a limited BESS, the power dispatch capability should be defined primarily at beginning of each dispatching interval to cooperate with the transmission system operator. This study introduces a method to determine the power dispatch capability of a wind-battery hybrid power system with the minimal battery capacity. The power dispatch capability is decided under the condition that the battery power is maintained below its rating and that the state of charge of the battery is guaranteed within a safe range. For economic BESS operation, the battery capacity, including the power and energy ratings, is also determined cost-effectively to obtain the constant power dispatch. Based on the long-term wind power profile, the authors develop the determination process to minimise the battery capacity which ensures that the BESS sufficiently mitigates wind power variation to dispatch the constant power into grid under all wind conditions. To evaluate the proposed determination method, a case study is carried out with a 3-MW wind turbine generator and real wind speed data measured on Jeju Island in Korea.

This study presents the distributed model predictive control (D-MPC) of a wind farm equipped with fast and short-term energy storage system (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines (WTs) and the ESS unit, the WT mechanical loads are alleviated. With the fast gradient method, the convergence rate of the D-MPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger WT load reduction, compared with the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS.

This study presents technical issues to enhance the micro-grids (MGs) performance. Novel distributed flexible AC transmission system (D-FACTS) is applied on AC/DC MGs to achieve full utilization of distributed generations (DGs), and to improve power quality and system stability. That D-FACTS is called green plug-energy economizer (GP-EE) device, which is proposed with two schemes to be applied on both DC/AC terminals of MG. DGs are used within MGs so that they can meet dynamic load patterns. Proposed design of MG includes photovoltaic, fuel cell, battery, storage system, micro-gas turbine and wind turbine. GP-EE device will be controlled by PID modified weighted controller. Dynamic tri-loop error driven will be enhanced by extra supplementary regulation loops to ensure power factor correction, stabilize buses voltage, reduce feeder losses and power quality enhancement. A recent heuristic optimization technique is called backtracking search algorithm, and is proposed for optimal selection of the scheme parameters of pulse-width modulation pulsing stage of GP-EE to dynamically online gains adjustment of PID tri-loop regulation process and minimization of the global control error. Digital simulations have been validated GP-EE schemes effectiveness based on without and with modules performance. Achieved results show applicability and improved performance using the proposed technique.

The rising climatic concerns force the conventional power system to minimise the gas emissions. Microgrid integrating with renewable energy plays a key role in utilising power systems in a more environment friendly way. Compared with single renewable energy system, the microgrid with multi-energy systems is more attractive in practice. The schedule problem is considered to schedule wind power, combined heating and power generation (CHP), battery, and power grid in order to satisfy the electricity load and heat loads in the microgrid with the minimal cost in real time. Owing to the stochastic nature of wind power generation, it is a challenge to solve the multi-energy scheduling problem when using the obtained real-time information. In this study, the joint scheduling of the microgrid with multi-energy systems is formulated as a rolling horizon Markov decision process (MDP). Moreover, then the rollout algorithm is applied to solve the problem in order to deal with the large state and decision space of MDP, which is caused by the increase of the number of CHPs. The feasible base policy needed in the rollout algorithm is constructed using a greedy algorithm. Numerical results demonstrate that the algorithm can achieve the requirement of real-time scheduling effectively.

This study presents a novel analysis of the utilisation of grid scale energy storage to mitigate negative system operational impacts due to high penetrations of wind power. This was investigated by artificially lowering the minimum stable generation level of a gas thermal generating unit coupled to a storage device over a five hour storage charging window using a unit commitment and economic dispatch model. The key findings of the analysis were a 0.18% reduction in wind curtailment, a 2.35 MW/min reduction in the ramping rate required to be met by all generators in the test system during a representative period and a total generation cost reduction of €6.5 million.

Future power systems are expected to integrate large-scale stochastic and intermittent generation and load due to reduced use of fossil fuel resources, including renewable energy sources and electric vehicles (EV). Inclusion of such resources poses challenges for the dynamic stability of synchronous transmission and distribution networks, not least in terms of generation where system inertia may not be wholly governed by large-scale generation but displaced by small-scale and localised generation. Energy storage systems (ESS) can limit the impact of dispersed and distributed generation by offering supporting reserve while accommodating large-scale EV connection; the latter (load) also participating in storage provision. In this study, a local energy storage system (LESS) is proposed. The structure, requirement and optimal sizing of the LESS are discussed. Three operating modes are detailed, including: (i) storage pack management; (ii) normal operation; and (iii) contingency operation. The proposed LESS scheme is evaluated using simulation studies based on data obtained from the Northern Ireland regional and residential network.

This study proposes a novel optimal generation scheduling model for virtual power plant (VPP) considering the degradation cost of energy storage system (ESS). The VPP is generally formed by a mix of distributed energy resources, and the ESS is an important installation for flexible VPP dispatch due to its controllable and schedulable behaviours. For the operations of battery storage systems, the ambient temperature and depth of discharge have significant impacts on the wear and tear of the ESS as well as battery degradation cost. Furthermore, the battery degradation cost is modelled and approximated by a piecewise linear function, and then incorporated into the proposed optimal VPP scheduling model. Consequently, the optimal VPP scheduling problem is formulated as a two-stage stochastic mixed-integer linear programming in order to maximise the expected profits of the VPP. The proposed model has been successfully implemented and tested through a representative case study, and the influence of battery degradation cost on optimal VPP scheduling has also been thoroughly analysed and demonstrated.

This study develops a methodology for coordinated operation of distributed energy storage systems in distribution networks. The developed methodology considers that energy storage resources can contribute to their owners’ inherent activities and to a more flexible and efficient distribution network operation. The optimisation tool based on mixed-integer linear programming is developed to maximise the technical and economic value of distributed storage taking into account their multifunctional potential and the presence of intermittent renewable sources. The methodological developments are validated in a case study of a real medium-voltage distribution network with two storage systems coupled with a wind park and an industrial ‘prosumer’ (i.e. energy consumers who are producing their own energy). Results make evident the robustness of the methodology and enable the assessment of the technical and economic impacts of the integration of distributed storage. Moreover, opportunity costs for distributed storage to perform services to the distribution system operator are demonstrated.

This study presents a novel hybrid operation strategy for a wind energy conversion system (WECS) with a battery energy storage system (BESS). The proposed strategy is applied to support frequency regulation using coordinated control of WECS and BESS operations in power system. The coordinated control of the WECS consists of active power control and frequency support control based on permanent magnet synchronous generators for variable wind speed conditions. Active power control is achieved using maximum power point tracking and deloaded operation to ensure a certain power margin. In addition to this comprehensive control of the active power, frequency support control based on kinetic energy discharge control is developed to regulate the short-term frequency response and to ensure reliable operation of the power system. Concurrently, the output power command of the BESS is determined according to the state of charge and frequency deviations. The effectiveness of the hybrid operation strategy is verified by a numerical simulation in power system computer aided design/electro-magnetic transient direct current (PSCAD/EMTDC), and the results show that the proposed approach can improve the frequency regulation capability of the power system.

Virtual power plant (VPP) concept was developed to integrate distributed energy resources (DERs) into the grid in order that they are seen as a single power plant by the market and power system operator. Therefore, VPPs are faced with optimal bidding, and identifying arbitrage opportunities in a market environment. In this study, the authors present an arbitrage strategy for VPPs by participating in energy and ancillary service (i.e. spinning reserve and reactive power services) markets. On the basis of a security-constrained price-based unit commitment, their proposed model maximises VPP's profit (revenue minus costs) considering arbitrage opportunities. The supply–demand balancing, transmission network topology and security constraints are considered to ensure reliable operation of VPP. The mathematical model is a mixed-integer non-linear optimisation problem with inter-temporal constraints, and solved by mixed-integer non-linear programming. The result is a single optimal bidding profile and a schedule for managing active and reactive power under participating in the markets. These profile and schedule consider the DERs and network constraints simultaneously, and explore arbitrage opportunities of VPP. Results pertaining to an illustrative example and a case study are discussed.

This study presents the modelling and dynamic simulation of a high penetration wind diesel power system (WDPS) consisting of a diesel generator (DG), a wind turbine generator (WTG), consumer load, dump load and a battery energy storage system (BESS). First the WDPS architecture and the models of the WDPS components are described. The WDPS is simulated in wind-only (WO) mode where the DG is not running and the WTG supply active power and in wind-diesel (WD) mode where both DG and WTG supply power. The simulation results are given showing graphs of the main electric variables in the WDPS (system frequency and voltage and active power in each component) and main battery variables (current, voltage and state of charge). The results in the WO mode show how the BESS, under the command of a proportional-integral-derivative (PID) controller, supplies/stores active power to regulate the isolated system frequency. The WD control enables the BESS to smooth the load and wind power variations, so that the isolated system power quality is improved. Also it is shown in the WD mode a peak shaving application where the control orders the BESS to supply active power temporarily to support system frequency in a DG overload situation.

The energy stored in the batteries of electric vehicles (EVs) could be employed for starting generators when a blackout or a local outage occurs. Considering the feature of the battery swapping mode, an available capacity model of the batteries in a centralised charging station is first developed. Then, the authors analyse the start-up characteristics of a generator powered by batteries and propose a bi-level optimisation-based network reconfiguration model to determine the restoration paths with an objective of maximising the overall generation capability. In the upper-level optimisation model, the generator start-up sequence is optimised, whereas the restoration paths are optimised in the lower-level one. Moreover, they consider the uncertainties associated with the available capacity of the batteries. The bi-level optimisation model for the network reconfiguration is developed in the chance-constrained programming framework and solved by the well-established particle swarm optimisation algorithm. Finally, case studies are employed to demonstrate the effectiveness of the presented model. Simulation results show that a centralised EV charging station could act as a power source to effectively restore a power system without black-start (BS) generators or with insufficient cranking power from BS generators, and the presented model could be used to guide actual system restorations.

This study presents a control strategy for the frequency regulation in a wind–diesel powered microgrid. With wind as a major energy resource, ensuring reliability and quality of power supplied in the system is a great challenge. To reduce the adverse effects caused by wind's variability, intermittency and uncertainty on the system frequency and improve the performance of diesel generator (DG), a solution is explored that involves the use of two different energy storage technologies. A test system is proposed consisting of a wind farm and a DG, supplemented by hydrogen storage with fuel cell (FC) as a long-term and a flywheel (FW) as a short-term energy storage. During low demand or high wind periods, the surplus energy generated is stored as kinetic energy in the FW and as hydrogen gas after water electrolysis. During periods of low wind speed or increased demand, the FW supplies energy by shedding its rotor speed and hydrogen is converted into electricity through the FC. The effectiveness of adding a short-term and a long-term energy storage in enhancing the robustness of wind–diesel system is demonstrated in this study.

In this study, optimal scheduling of multiple non-colocated, price taker, independent wind power producers (WPPs) participating in forward day-ahead (DA) distribution electricity market is described; where, a WPP is comprised of multiple wind turbine generator (WTG) and battery storage device (BSD). Cost equivalent of reduction in network losses and improvement in voltage profile for non-colocated placement of WTG and BSD in Distribution Network (termed as ancillary benefit) is included in the objective function resulting in a scheduling strategy dependent upon location of WPP in the network. Objective function comprises of following sub-objectives: (i) maximize return from energy market, (ii) maximize benefit obtained from providing ancillary services, and (iii) minimize uncertainties in schedule by providing reserve from BSDs. Non-linear programming (NLP) technique is used for scheduling. Location of a WPP is varied to obtain a ‘profit map’; which can be used as an ‘offline-tool’ to find out relative location of WTG and BSD for profit maximization. Proposed formulation is extended to participation of multiple WPP, where ancillary benefit is proportionally shared. Wind power forecast uncertainty leads to risk of not meeting the schedule and is probabilistically modeled in this work. Impact of reserve on DA energy schedule of is also studied.

Electrical energy storage provides a potential solution to the challenge of integrating large amounts of intermittent renewable energy into the electricity system. To make storage commercially viable, its operators will have to aggregate multiple revenue streams across the electricity industry. Arbitrage is recognised as one potential revenue stream. To date, wind power has provided a small contribution toward electricity generation in Great Britain (GB). Gas generators have delivered a significant proportion of total demand. Historic electricity prices reflect this, being driven principally by variations in gas price and daily demand cycles. The study reported here investigates the potential impact of wind power on electricity prices and arbitrage opportunities for energy storage in GB. Results indicate that increased wind power leads to higher price volatility for low electricity prices, but reduced frequency of higher prices which may be detrimental to storage revenue.

Variability of wind power is one of the main concerns of power system operation with significant wind power. Energy storage can be employed in conjunction with wind power to reduce the uncertainty associated with wind power. This study assumes the storage facility being operated by the wind farm operator for the explicit purpose of minimising the risk of wind power commitment. A time-dependent wind power model is developed in conjunction with the energy storage model for short operating lead times that are conditional on the initial wind and storage conditions. This study presents a probabilistic method to assess the reliability contribution of energy storage using two probabilistic indices: the wind power commitment risk and the unit commitment risk.

The increasing deployment of non-dispatchable generation in electric systems where generation and demand must be balanced at all times has led to a renewed interest in technologies for energy storage. This study presents a cost–benefit analysis of energy storage for peak demand reduction in medium-voltage distribution networks. In particular, the installation of batteries in secondary substations is studied for three realistic large-scale networks representing urban, semi-urban and rural distribution areas. On the one hand, savings in energy costs derived from storing energy at low-priced hours and selling it at peak hours are considered. On the other hand, savings in network reinforcement due to the peak shaving are evaluated. Network reinforcement requirements are assessed using reference network models, large-scale network-planning tools often used by distribution regulators to establish the allowed distribution costs. Additionally, sensitivity to different demand growth ratios and battery capacities is analysed. The final objective is to determine the target cost for batteries to be profitable from the point of view of distribution. Results show that significant savings can be obtained, especially in urban and semi-urban areas.

Rapid expansion and integration of wind energy is restrained due to transmission capacity constraints and conventional generation technologies limited operational flexibility in today's power systems. Energy storage is an attractive option to integrate and utilise more renewable energy without major and timely upgrade of existing transmission infrastructure. Moreover, it can be considered as a means for differing the reinforcement plans. The evaluation of energy storage deployment projects is a challenging task due to severe uncertainty of wind power generation. In this study, a robust techno-economic framework is proposed for energy storage evaluation based on information gap decision theory for handling wind generation uncertainty. The total social cost of the system including conventional generators’ fuel and pollution cost and wind power curtailment cost is optimised considering generators operational constraints and transmission system capacity limitations based on the DC model of the power grid. The effect of storage devices on system performance is evaluated taking into account wind power uncertainty. The proposed method is conducted on the modified IEEE reliability test system and the modified IEEE-118-bus test system to assess its applicability and performance in mid-term robust evaluation of energy storage implementation plans.

This study presents a simple methodology for analysing and optimising combined wind generation and storage schemes, using both technical and economic performance criteria. The study provides a detailed analysis of the performance of two storage options for such a scheme: pumped storage hydro (PSH) and battery energy storage systems (BESSs). The analysis is carried out using recorded data from an actual UK wind farm (WF), information on the UK electricity market, and currently available PSH and BESS storage technologies to estimate and compare performance of the considered wind generation–storage schemes over the entire lifetime. The results show that an optimised generation–storage scheme can significantly reduce the variability of power outputs and increase the profitability of the WF. It is further shown that optimised PSH-based schemes have better economic performance than BESS schemes, as the latter are limited by the short discharge times. The approach developed in this study could be used during the initial design and planning stages, in order to select and optimise the type and size of energy storage for a combined wind generation–storage scheme.