Long-term revenue estimation for battery performing arbitrage and ancillary services

International audienceEnergy storage revenue estimation is essential for analyzing financial feasibility of investment in batteries. We quantify the cycles of operation considering depth-of-discharge (DoD) of operational cycles and provide an algorithm to calculate equivalent 100% DoD cycles. This facilitates in comparing cycles of different DoDs. The battery life is frequently defined as a combination of cycle and calendar life. We propose a battery capacity degradation model based on the cycle and the calendar life and operational cycles. Using equivalent 100% DoD cycles and revenue generated, we calculate the dollars per cycle revenue of storage performing electricity price based arbitrage and ancillary services for load balancing in real time. Using PJM's (a regional transmission organization in the United States) real data we calculate short term and long term financial potential for the year of 2017. We observe that participating in ancillary services is significantly more beneficial for storage owners compared to participating in energy arbitrage

Maximising the value of electricity storage

Grid-scale energy storage promises to reduce the cost of decarbonising electricity, but is not yet economically viable. Either costs must fall, or revenue must be extracted from more of the services that storage provides the electricity system. To help understand the economic prospects for storage, we review the sources of revenue available and the barriers faced in accessing them. We then demonstrate a simple algorithm that maximises the profit from storage providing arbitrage with reserve under both perfect and no foresight, which avoids complex linear programming techniques. This is made open source and freely available to help promote further research. We demonstrate that battery systems in the UK could triple their profits by participating in the reserve market rather than just providing arbitrage. With no foresight of future prices, 75-95% of the optimal profits are gained. In addition, we model a battery combined with a 322 MW wind farm to evaluate the benefits of shifting time of delivery. The revenues currently available are not sufficient to justify the current investment costs for battery technologies, and so further revenue streams and cost reductions are required

Control Analysis for Grid Tied Battery Energy Storage System for SOC and SOH Management

Frequency regulation is an important part of grid ancillary services in the UK power system to mitigate the impacts of variable energy resources and uncertainty of load on system frequency. The National Grid Electricity Transmission (NGET), the primary electricity transmission network operator in the UK, is introduced various frequency response services such as firm frequency response (FFR) and the new fast enhanced frequency response (EFR), which are designed to provide real-time response to deviations in the grid frequency. Flexible and fast response capabilities of battery energy storage systems (BESSs) make them an ideal choice to provide grid frequency regulation. This thesis presents control algorithms for a BESS to deliver a charge/discharge power output in response to deviations in the grid frequency with respect to the requisite service specifications, while managing the state-of-charge (SOC) of the BESS to optimize the availability of the system. Furthermore, this thesis investigates using the BESS in order to maximize triad avoidance benefit revenues while layering UK grid frequency response services. Using historical UK electricity prices, a balancing service scheduling approach is introduced to maximize energy arbitrage revenue by layering different types of grid balancing services, including EFR and FFR, throughout the day. Simulation results demonstrate that the proposed algorithm delivers both dynamic and non-dynamic FFR and also EFR to NGET required service specifications while generating arbitrage revenue as well as service availability payments in the balancing market. In this thesis, a new fast cycle counting method (CCM) considering the effect of current rate (C-rate), SOC and depth-of-discharge (DOD) on battery lifetime for grid-tied BESS is presented. The methodology provides an approximation for the number of battery charge-discharge cycles based on historical microcyling SOC data typical of BESS frequency regulation operation. The EFR and FFR algorithms are used for analysis. The obtained historical SOC data from the analysis are then considered as an input for evaluating the proposed CCM. Utilizing the Miner Rule’s degradation analysis method, lifetime analysis based on battery cycling is also provided for a lithium-titanate (LTO) and lithium-nickel-manganese-cobalt-oxide (NMC) battery. The work in this thesis is supported by experimental results from the 2MW/1MWh Willenhall Energy Storage System (WESS) to validate the models and assess the accuracy of the simulation results

Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, Self-Sufficiency, Peak Shaving and Energy Backup

International audienceEnergy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long timescale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average (ARMA) forecasting along with Model Predic-tive Control (MPC) for real-time implementation of the proposed optimization problem in the presence of uncertainty

The Potential for Energy Arbitrage Using Battery Energy Storage Systems in Norwegian Power Markets : An Economic Viability Study through Financial Valuation

arbitrage, the process of storing energy when prices are low and offering it when prices are high, has, through increased electricity prices and price volatility, shown greater economic potential over the past couple of years. In light of these developments, this study analyzes the economic viability, through a financial valuation, of a 10MW/10MWh Battery Energy Storage System (BESS) performing energy arbitrage in the Norwegian power markets over a 30-year project. To account for the latest developments in electricity prices and evaluate the economic viability of the BESS, the study incorporates 2022 electricity price data. Furthermore, the analysis includes electricity price data from the period of 2016-2019 to assess the BESS's economic viability in the event of a return to historically “normal” Norwegian electricity prices. The study aims to present a comprehensive and holistic valuation of the BESS through the inclusion of all factors affecting the profits generated and the related costs of performing the energy arbitrage. The optimal energy arbitrage trading pattern is identified through a Mixed-Integer Nonlinear Programming (MINLP) model, and the resulting trading profits are valued through a Discounted Cash Flow (DCF) encompassing all relevant expenditures. The discount rate in the DCF is derived from an estimated Weighted Average Cost of Capital based on a Comparable Companies Analysis. The results from the analysis show that a BESS performing energy arbitrage in the Norwegian power markets is not economically viable with the current BESS cost estimations and power market conditions. The results for the 2022 electricity price scenario show the greatest promise in the southern price zones of Norway due to the historically high electricity prices and price volatility. However, the Net Present Value (NPV) of the cash flows for the BESS in the best performing price zone is still significantly negative. With optimal trading profits of 39.6 MNOK, the best performing project generates a NPV of -120.4 MNOK when considering all Capital Expenditure (CAPEX), Operations and Maintenance (O&M) costs, and trading profits. When utilizing 2016-2019 electricity price data, the results worsen significantly due to the lower electricity price and price volatility in the period, resulting in a total trading profit of 2.3 MNOK and a total NPV of -157.7 MNOK for the BESS in the best performing price zone.nhhma

Integration of Energy Storage into a Future Energy System with a High Penetration of Distributed Photovoltaic Generation

Energy storage units (ESU) are increasingly used in electrical distribution systems because they can perform many functions compared with traditional equipment. These include peak shaving, voltage regulation, frequency regulation, provision of spinning reserve, and aiding integration of renewable generation by mitigating the effects of intermittency. As is the case with other equipment on electric distribution systems, it is necessary to follow appropriate methodologies in order to ensure that ESU are installed in a cost-effective manner and their benefits are realized. However, the necessary methodologies for integration of ESU have not kept pace with developments in both ESU and distribution systems. This work develops methodologies to integrate ESU into distribution systems by selecting the necessary storage technologies, energy capacities, power ratings, converter topologies, control strategies, and design lifetimes of ESU. In doing so, the impact of new technologies and issues such as volt-VAR optimization (VVO), intermittency of photovoltaic (PV) inverters, and the smart PV inverter proposed by EPRI are considered. The salient contributions of this dissertation follow. A unified methodology is developed for storage technology selection, storage capacity selection, and scheduling of an ESU used for energy arbitrage. The methodology is applied to make technology recommendations and to reveal that there exists a cost-optimal design lifetime for such an ESU. A methodology is developed for capacity selection of an ESU providing both energy arbitrage and ancillary services under a stochastic pricing structure. The ESU designed is evaluated using ridge regression for price forecasting; Ridge regression applied to overcome numerical stability and overfitting issues associated with the large number of highly correlated predictors. Heuristics are developed to speed convergence of simulated annealing for placement of distributed ESU. Scaling and clustering methods are also applied to reduce computation time for placement of ESU (or any other shunt-connected device) on a distribution system. A probabilistic model for cloud-induced photovoltaic (PV) intermittency of a single PV installation is developed and applied to the design of ESU