Energy Storage Optimization Strategies for Smart Grids (original) (raw)
Related papers
2020
The purpose of present research is an analysis of currently promoted energy storage systems based on high-capacity electric batteries from the standpoint of algorithms for intelligent control of their charge and discharge processes. It is discussed the reduction the cost of electricity consumed by the enterprise by the redistributing of energy depending on the variation in tariffs over time. It is based on the use of the Energy Storage System (ESS) and optimal battery charge/discharge schedule. An estimation of savings in consumed energy costs is carried out depending on the power, capacity of ESS, as well as of the period of planned schedule calculating. On base of numerical simulation of battery’s charge/discharge control by linear programming optimisation method the efficiency of ESS usage was estimated in the range 10–15% for different periods from 1 up to 5 days of scheduling (planning horizon) respectively.
Optimal demand-side management with a multi-technology battery storage system
Renewable Energy and Power Quality Journal, 2018
Demand-side management (DSM) is considered as a key solution for more energy system flexibility, which is needed for the transition to low-carbon electricity generation based on variable renewable resources. Increased flexibility reduces energy bills for customers and congestions in electricity transport and distribution networks, which reduces costs for network operators, as demand is matched with available renewable generation. Recently, smart-meter deployment, real-time pricing and cost reductions for electricity storage opened new opportunities for dynamic DSM optimization tools. This paper describes software and hardware tools and a low-cost energy storage system (ESS) to elaborate demand management programs, which reduce the energy bill of industrial customers. These tools operate at two levels: remotely, to calculate the economically optimal consumption and ESS operation program and locally to adapt the economic program to the real-time user state. The described tools have been developed within a national Spanish research project called EV-OPTIMANAGER, which was co-funded by the Spanish Government through the "Retoscolaboración 2015" research program.
Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation
Energy storage systems have the potential to deliver value in multiple ways, and these must be traded off against one another. An operational strategy that aims to maximize the returned value of such a system can often be significantly improved with the use of forecasting – of demand, generation, and pricing – but consideration of battery degradation is important too. This paper proposes a stochastic dynamic programming approach to optimally operate an energy storage system across a receding horizon. The method operates an energy storage asset to deliver maximal lifetime value, by using available forecasts and by applying a multi-factor battery degradation model that takes into account operational impacts on system degradation. Applying the method to a dataset of a residential Australian customer base demonstrates that an optimally operated system returns a lifetime value which is 160% more, on average, than that of the same system operated using a set-point-based method applied in many settings today.
Intelligent Industrial Systems, 2015
In the wholesale energy market, electricity prices are determined by the balance between supply and demand. Normally, customers are not exposed to these variations but pay a constant electricity price. In an attempt to reduce demand peaks, several utilities are moving from a conventional fixed-rate pricing scheme to a new market-based model, based on time-of-use or real-time pricing, able to closely reflect the wholesale energy price. Electricity customers can thus take profit from the installation of storage systems, shifting their energy consumption from on-peak to off-peak periods. This paper presents a novel charging strategy to manage customer storage systems in presence of hourly electricity prices. The optimal operating schedule of the storage device is obtained by maximizing an objective function which corresponds to the maximum benefit for the storage owner. The proposed method is developed under the assumption that the operating scheduling of the battery energy storage system (BESS) does not depend on the specific facility's consumption. The model can be applied to several kinds of storages although the simulations refer to a lead-acid battery. Test results show that the proposed B Enrico Telaretti
Model Predictive Optimization for Energy Storage-Based Smart Grids
Communications in Computer and Information Science
In recent years, energy storage systems (ESS) have started to play the role of an active electricity supplier so as to minimize overall electricity costs in a smart grid. However, ESS lifetime decreases with each cycle of charge/discharge. There is a tradeoff between ESS lifetime and electricity cost saving. As a solution, this work proposes a Model Predictive Optimization (MPO) method for distribution management in smart grids. Future energy states are predicted using an Autoregressive Integrated Moving Average (ARIMA) model. Based on the predicted electricity status, a near-optimal schedule for ESS usage is found using Genetic Algorithm such that a tradeoff is made between cost saving from electricity trading and the loss of life (LoL) in ESS. Experiment results show that the error rate of the prediction model is less than 10%. The MPO method achieves an overall cost saving of 0.85% and an ESS LoL reduction of 12.18%.
Applied Sciences
Premature ageing of lithium-ion battery energy storage systems (BESS) is a common problem in applications with or without renewable energy sources (RES) in the household sector. It can result to significant issues for such systems such as inability of the system to cover load demand for a long period of time. Consequently, the necessity of limiting the degradation effects at a BESS leads to the development and application of energy management strategies (EMS). In this work, EMSs are proposed in order to define optimal operation of a BESS without RES under time-of-use (ToU) tariff conditions. The objective of the developed EMSs is to reduce the capacity loss at the BESS in order to extend its lifetime expectancy and therefore increase the economic profit in the long-term. The EMSs utilize a widely used battery mathematical model which is experimentally validated for a specific BESS and a battery degradation mathematical model from the literature. Indicative simulation results of the ...
2017 North American Power Symposium (NAPS), 2017
The transformation of today's grid toward smart grid has given the energy storage systems (ESSs) the opportunity to provide more services to the electric grid as well as the end customers. On the grid's side, ESSs can generate revenue streams participating in electricity markets by providing services such as energy arbitrage, frequency regulation or spinning reserves. On the customers' side, ESSs can provide a wide range of applications from on-site backup power, storage for offgrid renewable systems to solutions for load shifting and peak shaving for commercial/industrial businesses. In this work, we provide an economic analysis of behind-the-meter (BTM) ESSs. A nonlinear optimization problem is formulated to find the optimal operating scheme for ESSs to minimize the energy and demand charges of time-of-use (TOU) customers, or to minimize the energy charge of net-metering (NEM) customers. The problem is then transformed to Linear Programming (LP) problems and formulated using Pyomo optimization modeling language. Case studies are conducted for PG&E's residential and commercial customers in San Francisco.
Battery Storage Systems in Smart Grid Optimised Buildings
Energy Procedia, 2018
The building sector is responsible for a significant proportion of the consumed energy and the consequent carbon emissions. Currently, electricity and natural gas are the most popular fuels used in the UK Services sector and the industry. Furthermore, buildings constitute a key component of the power network, in both its current conventional form and its evolution, the smart grid. The smart grid is expected to integrate energy storage, distributed generation and buildings into the network. This paper introduces the concept of Smart Grid Optimised Buildings (SGOBs), recognising the importance of energy storage to establish a dynamic interaction between the building and the smart grid. SGOBs are expected to be fully electric, make the best use of the available resources and utilise their embedded battery storage systems to respond to notifications issued by the smart grid and to dynamic electricity prices. Assuming that buildings have access to the day-ahead electricity market, initial results show that battery storage can be successfully used to change a building’s electricity profile and perform load-shifting (arbitrage) and peak-shaving while the excess electricity is exported back to grid to take advantage of the price difference and relieve pressure on the infrastructure.
Dispatch Strategies for the Utilisation of Battery Storage Systems in Smart Grid Optimised Buildings
Buildings, 2021
This study investigates Smart Grid Optimised Buildings (SGOBs) which can respond to real-time electricity prices by utilising battery storage systems (BSS). Different building design characteristics are assessed to evaluate the impact on energy use, the interaction with the battery, and potential for peak load shifting. Two extreme cases based on minimum and maximum annual energy consumption were selected for further investigation to assess their capability of utilising BSS to perform arbitrage, under real-time pricing. Three operational dispatch strategies were modelled to allow buildings to provide such services. The most energy-efficient building was capable of shifting a higher percentage of its peak loads and export more electricity, when this is allowed. When using the biggest battery (220 kWh) to only meet the building loads, the energy-efficient building was able to shift 39.68% of its original peak loads in comparison to the 33.95% of the least efficient building. With expo...
Maximising the value of electricity storage
Journal of Energy Storage, 2016
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.