• Energy Research

Abstract This paper evaluates the economic potential of energy flexibility in 50 different German small and medium sized enterprises (SMEs) through the installation of a battery storage system (BSS). The central innovation lies in the possibility of pursuing multiple revenue streams simultaneously: peak shaving, provision of primary control reserve (PCR) and energy-arbitrage-trading through intraday and day-ahead markets. The energy system of an industrial manufacturing plant is modelled as a linear program (LP) with a 15-min resolution. The model offers the option to invest in BSSs with different capacities, with the objective of minimizing the overall cost and identifying the optimal size of the BSS. The results show that none of these three revenue streams individually is economically attractive, but when combined, all three together can achieve profitability for some companies, whereby the majority of the cash flow comes from peak shaving and PCR. With a fixed BSS capacity of 500 kWh, the Net Present Value (NPV) varies from a minimum of −350,000 € for just arbitrage up to about 200,000 € for all three use cases in parallel. In the case of a variable BSS capacity, the capacity varies up to 1200 kWh and the Profitability Index (the ratio of investment to NPV) varies from 0.06 to 0.31. Under current German market conditions, arbitrage trading contributes only marginally to the profitability, as the price spreads are too small to justify stronger battery degradation. The paper also identifies various load indicators from the analysis of the demand profile that support the evaluation of a BSS in industry. A stepwise linear regression reveals a moderate dependency of the BSS profitability on two newly developed load indicators. Future work should focus on a more detailed depiction of the battery’s technical behaviour and increasing the sample size to improve the statistical significance of the results.

Abstract Electric vehicles (EV) are treated as a breakthrough technology in the automotive market. The novelty of this technology also implicates that the incidence of these vehicles worldwide is still low. An important issue regarding EVs is the existence of proper charging infrastructure as waiting at charging stations due to an inadequate number of chargers can discourage EV owners. However, as the number of EVs and charging stations are low at present, real world experience is not available, so computer simulations are required for the planning of such charging stations. We developed a stochastic model in this paper that includes driving and charging behaviour of EV owners in Japan. The model is based on Monte Carlo methods and was implemented in MATLAB. We conducted simulations with this model to find out whether the existing infrastructure is adequate for the charging of a large number of EVs. The results indicate that Japan is well prepared for an increase in plug-in vehicles (PHEVs) in the near future: currently the country has 6 fast chargers for 100 electric cars and for this ratio - on average -, waiting probability at DC (direct current) fast chargers ranges lower than 5%, which is an acceptable value for EV owners. If, however, the ratio decreases, waiting probability increases exponentially.