Optimum community energy storage system for PV energy time-shift

Highlights

• The performance and economic benefits of Pb-acid and Li-ion batteries are compared.

• The business case during the decarbonisation pathway is assessed.

• The aggregation from a community approach reduced the levelised cost by 37% by 2020.

• For a forecast price of 16.3 p/kW h Li-ion battery cost must be less than 275 £/kW h.

• A 10% subsidy will be needed for Li-ion batteries to achieve the 2020 forecast.

Abstract

A novel method has been designed to obtain the optimum community energy storage (CES) systems for end user applications. The method evaluates the optimum performance (including the round trip efficiency and annual discharge), levelised cost (LCOES), the internal rate of return and the levelised value of suitable energy storage technologies. A complimentary methodology was developed including three reference years (2012, 2020 and zero carbon year) to show the evolution of the business case during the low carbon transition. The method follows a community approach and the optimum CES system was calculated as a function of the size of the community. In this work, this method was put in practice with lead-acid (PbA) and lithium-ion battery (Li-ion) technologies when performing PV energy time-shift using real demand data from a single home to a 100-home community. The community approach reduced the LCOES down to 0.30 £/kW h and 0.11 £/kW h in 2020 and the zero carbon year respectively. These values meant a cost reduction by 37% and 66% regarding a single home. Results demonstrated that PbA batteries needs from 1.5 to 2.5 times more capacity than Li-ion chemistry to reduce the LCOES, the worst case scenario being for the smallest communities, because the more spiky demand profile required proportionately larger PbA battery capacities.

Introduction

The European Union aims to cut greenhouse gas emissions to 20% below 1990 levels by 2020 as part of the strategic 2050 roadmap [1]. Other countries such as USA and Japan have similar objectives. From the generation supply side, renewable energy (RE) technologies and the use of flexible vectors such as electricity, heat and hydrogen are some of the key technologies to achieve these ambitious objectives. Among different RE technologies, solar PV is expected to play an important role during this transition pathway and its rise has already started. PV energy was the fastest-growing power technology worldwide between 2000 (1.5 GW_p cumulative capacity) and 2010 (40 GW_p cumulative capacity) with 7.4 GW installed in Germany alone [2]. It is the most widespread power technology in the built environment due to its modularity, free-maintenance and quiet performance [3]. Additionally, continuous progress has reduced the system cost dramatically (according to the IEA, the costs of PV exhibits a learning rate of 19.3% being defined as the reduction of cost for every doubling of global capacity) and the efficiency has increased steadily (from about 12% to 15% for commercial crystalline silicon panels [4]). The PV penetration in the UK was 1.7 GW_p in September 2013, with 1.3 GW_p (77%) from installations with a capacity lower than 4 kW_p [5].

In the traditional electrical network, the electricity flows from centralized fossil generation plants to the point of consumption. Coal, natural gas and diesel generation plants usually offer a certain level of schedule therefore they can be considered as load following generators. Typically, nuclear plants run at more constant power, supplying a base load. The penetration of PV and other RE technologies will affect the whole energy system which was designed and built according to flexibility offered by fossil fuels. However, PV technology and other RE technologies depend on the weather conditions. This means that they do not offer the same level of demand matching capability as traditional generators do as it is not possible to forecast with total accuracy the PV power output [6].

PV generation follows daily and seasonal patterns proportional to the local irradiance and this behaviour is more marked at latitudes further from the equator. In the case of the built environment, there can be a mismatch between the PV generation and the local demand e.g. the annual mismatch for a 4.5 kW_p PV installation and the electrical load of a single dwelling was found to be 81% [7]. However, for energy communities with several dwellings, it has been found that the mismatch between PV generation and domestic demand reduces with the number of households due to random load coincidence [8]. Another interesting finding was that for arrays with a rating up to 1 kW_p per household, almost all electricity produced is consumed by the local demand loads, reducing the losses in the distribution area. The ability of different PV array orientations, demand-side management tools and energy storage (ES) to improve the matching capability of distributed PV at high-latitude areas was compared by Widén et al. [9]. According to the findings, ES is the most effective technology to shift the PV generation to meet the demand load at high PV penetration levels. Therefore, understanding the cost, value and profitability of the ES in communities and their dependence on the performance of ES, is key for the deployment of ES and the penetration of more PV technology.