Bringing innovation to market: business models for battery storage

Power systems around the world have undergone significant transitions towards a decentralization and decarbonization with higher requirements on supply security and flexibility. Technology advancement helps to improve energy efficiency and bring down cost, which in turn promote the growth of battery storage internationally. Business models of battery storage remain vague given its early stages of development but it is clear that there is no universal business model for batteries given the breadth of applications. In this study, we review the main components of existing business models and highlight the areas to be strengthened in a novel business model. Business models should be distinguished at different scales (utility-scale; behind-the-meter application; community-island mode operation) addressing different needs (to replace existing system or to add new capacity). A successful business model of a battery storage system needs to take into account electricity system transition, market and regulatory barriers, among others. Last but not least, it is important to consider innovations in other technologies for the design of a business model

Assessing the Status of Electricity Generation in the Non-Interconnected Islands of the Aegean Sea Region

Assessment of the electricity generation status for Non-Interconnected Islands (NIIs) of the Aegean Sea region, excluding the electricity systems of Crete and Rhodes, is undertaken in the current study. The authors focus on the long-term analysis of thermal power generation characteristics and also on the challenges so far limiting the contribution of Renewable Energy Sources (RES) in covering the electricity needs of the specific area. According to the present analysis, due to the existing technical limitations, the annual RES shares in the electricity balance of NIIs of the Aegean Sea have since 2010 stagnated in the range of 15% to 18%. Moreover, the performance of thermal power stations for all 30 NII systems is evaluated on the basis of their utilization factor, associated fuel consumption and electricity production costs. The vast majority of these stations is characterized by low capacity factors in combination with high specific fuel consumption and high operational expenses that in the case of smaller scale island regions could even exceed 600€/MWh. At the same time, the authors discuss on the alternatives and encourage further investigation of novel, intelligent energy solutions, such as the smart microgrid and battery-based hybrid power station that are currently developed on the island of Tilos under the implementation of the TILOS Horizon 2020 program

Integrated grey relational analysis and multi objective grey linear programming for sustainable electricity generation planning

Sustainable energy generation is a key feature in sustainable development and among various sources of energy electricity due to some unique characteristics seems particularly important. Optimising electricity generation mix is a highly complex task and requires consideration of numerous conflicting criteria. To deal with uncertainty of experts’ opinions, inaccuracy of the available data and including more factors, some of which are difficult to quantify, in particular for environmental and social criteria, we applied grey relational analysis (GRA) with grey linguistic, and grey interval values to obtain the rank of each system. Then the obtained ranking were used as coefficients for a multi objective decision making problem, aimed to minimize the cost, import dependencies and emissions as well as maximizing the share of generation sources with better ranking. Due to existence of interval variables multi objective grey linear programming (MOGLP) method was used to solve the problem. Our results for the UK as a case study suggest increased role for all low carbon energy technologies and sharp reduction in the use of coal and oil. We argue that the integrated GRA–MOGLP approach provides an effective tool for the evaluation and optimisation of complex sustainable electricity generation planning. It is particularly promising in dealing with uncertainty and imprecisions, which reflect real-life scenarios in planning processes

Life cycle greenhouse gas emissions from power generation in China's provinces in 2020

Carbon intensity of power generation is an important indicator to show the direct competitiveness of electricity against the combustion of fossil fuels. In this study, we estimate the carbon intensities of power generation in China's provinces. Most provinces are likely to have a carbon intensity per unit of power generation between 500 and 700 g CO2/kWh in 2020, which justifies the progress of electrification from the power generation perspective. With the growing share of low carbon power generation, most provinces show trends of decline in carbon intensity between 2015 and 2020. However, some provinces are expected to see increase in carbon intensity due to increasing share of coal power generation in their power mixes. Coal is still a major growth contributor in most provinces, despite significant growths of low carbon energy sources. Furthermore, renewable energy sources can help reduce the carbon intensity of power generation, but a better coordination among provinces is required, alongside with strong government support and direction

On the Value of Emerging, Day-Ahead Market Related Wind-Storage Narratives in Greece: An Early Empirical Analysis

Large-scale integration of renewable energy sources introduces high levels of uncertainty in power systems. In addressing the inherent uncertainty of renewables, coupling with energy storage systems allows for improved dispatchability, not only in terms of power system integration but also in terms of market participation. To that end, we currently look into the coupling of wind energy and energy storage and assess the ex-post value of different, day-ahead market related wind–storage narratives. In doing so, we apply practical dispatch strategies using empirical market signals, vary the size of storage, and adopt different cycling patterns, treating the configurations examined as price-taker units. In addition, by integrating different wind regimes and several years of spot price series, we argue that our approach captures different spatial and temporal characteristics; thus, offering a broad, representative view of the value and associated risk of similar market scenarios in the study area of Greece

Wind Energy and natural gas-based energy storage to promote energy security and lower emissions in island regions

Usually, isolated and remote areas, like islands, meet their electricity needs using oil-fired power generators. When available, natural gas can potentially substitute oil. Moreover, the high-quality wind energy potential found in many of these areas cannot be used extensively. Main reason is the operation of small-scale, weak electricity grids which cannot cope with wind energy intermittency. To compensate for that, we examine the combination of wind energy and energy storage. For the latter we focus on the technology of compressed air energy storage (CAES), which is suitable for scalable applications. To ensure the highest level of demand satisfaction, while avoiding system oversizing, we recommend a novel Wind–CAES system that allows switch from the CAES to the Brayton cycle when the stored energy is inadequate to meet demand. We develop a new algorithm for the sizing of such configurations, and use it on a case study that includes a typical, medium-scale Aegean Sea island in combination with three representative wind regimes. The results demonstrate that even in areas with relatively low-quality wind potential there are significant improvements in fuel use reduction, CO2 emissions and strengthening of energy supply security, while for island regions with higher-quality wind potential, the proposed solution also becomes cost-effective in comparison to other alternatives

Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems

Grid operators of islands with limited system tolerance are often challenged by the need to curtail wind energy in order to maintain system stability and security of supply. At the same time, and in the absence of storage facilities and/or other means of flexibility such as demand-side management, wind park owners face the problem of rejected wind energy production that varies considerably within the year. In the prospect of a more dynamic market operation in island grids, estimation of the anticipated wind energy curtailments may allow the evaluation of different options for wind park owners, such as short-term leasing of energy storage and/or direct, bilateral power purchase agreements with flexible demand entities. To enable such options, effective wind energy forecasting is necessary not only in terms of theoretical production, but also in terms of actual production being absorbed by the system. In this direction, the current research works on the prediction of day-ahead wind energy production in island grids, aiming to generate both theoretical (expected) and actual wind power forecasts. To that end, we use artificial neural networks for the development of different day-ahead forecasting models of hourly granularity, and we then test their performance in a large-scale non-interconnected island system, where annual wind energy curtailments for local wind parks may exceed 25% of the respective theoretical yield. Our results indicate that models developed provide a fair accuracy of day-ahead wind energy predictions, which is further elaborated by initiating a discussion on the emergence of alternative actor schemes in similar systems

Optimum operation of bulk energy storage systems in contemporary spot electricity markets

During recent years, there has been considerable interest in the research and development of energy storage systems (ESSs), with new technologies emerging and already established ones evolving further. Among the most established services that such systems can benefit from, arbitrage is admittedly corresponding to the most critical source of revenues. On the other hand, increased investment costs and energy conversion losses attached to ESSs usually lead to rather high electricity production costs that discourage investment in such projects. Considering the situation encountered, an effort is currently undertaken in order to develop an optimum arbitrage strategy for two different bulk ESSs, i.e. pumped hydro storage (PHS) and compressed air energy storage (CAES), in order to exploit any arbitrage opportunities appearing in contemporary electricity spot markets. For this purpose, by considering the special characteristics of each technology and by using historical data available from two different market regions of Nord Pool, a practical, deterministic dispatch strategy developed allows for the estimation of maximum net cash flows. According to the results obtained, optimum unit commitment ensures greater levels of revenues that may under certain conditions prove adequate for system operation in the long-run. Furthermore, comparison at both the regional market level and at the level of ESS provides useful information, first with regards to the spot market characteristics that may allow operation of energy storage under economically viable terms, and secondly with regards to the ability of PHS and CAES to support such investment efforts

Modelling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy

The recent literature on applied energy has emphasized the role of energy storage in the electricity supply chain. However, absence of an integrated valuation framework for services provided by energy storage technologies, owed to its limited scope so far, hinders investments in such capital intensive systems. To this end, the feasibility of an alternative operation strategy for energy storage systems (ESSs), based on the use of wind energy surplus, is currently investigated. More precisely, valuation of the policy options available to promote wind-based ESSs is carried out, by employing, for the first time, a comprehensive socioeconomic cost-benefit model that takes into account - among others - initial investment subsidies and feed-in tariffs (FiTs) for ESSs. Pumped hydro and compressed air energy storage covering peak demand are examined, demonstrating that such systems may prove cost-effective if "socially just" FiTs are applied, with our findings however, also supporting the notion that a portfolio of policies can more effectively facilitate investments in the sector