Storage Investment Research Articles

Can Distributed Intermittent Renewable Generation Reduce Future Grid Investments? Evidence from France

Abstract This paper estimates the relationship between investments in five distributed generation technologies and hourly net withdrawals from over 2,000 electricity distribution networks in France between 2005 and 2018. We find that investments in distributed wind and solar generation have little or no impact on the annual peak of hourly net withdrawals from the distribution grid, while investments in hydroelectric and thermal distributed generation significantly reduce it. An optimistic analysis of the impact of investments in battery storage suggests that high levels are required for distributed wind and solar to deliver similar reductions in the annual peak of hourly net withdrawals. Our results imply that public policies favoring distributed wind and solar generation over utility-scale generation cannot be rationalized by savings in future grid investments.

Uncertainty representation in investment planning of low-carbon power systems

Power system operators and planners are dealing both with the integration of unparalleled levels of variable renewable energy sources and deep uncertainties that originate from new technological developments, changing regulatory frameworks, unknown investment, operational costs of technologies, etc. An inadequate representation of the uncertainties may result in a substantial risk of deploying inflexible investment solutions incapable of adapting efficiently to evolving scenarios. In this context, this work studies the effects of increasing the granularity used to represent the long-term uncertainty by analysing its impact on the resulting optimal portfolios of new transmission lines, battery energy storage systems and pumped-hydro storage systems. The studies are conducted on an instance of the Australian power system described by the system operator for planning purposes, including four types of uncertainty granularity, namely deterministic representation, and 2-stage, 3-stage and 4-stage stochastic representations. To address the computational challenges associated with the large mixed-integer linear stochastic problems, the different instances are reformulated using Dantzig-Wolfe decomposition, enabling the use of a column generation approach to solve the investment problem. The case study applications show substantial adjustments in the investment portfolios as uncertainty granularity changes, with a clear tendency to increase battery storage investment as uncertainty is better represented.

The Usefulness of a Targeted Next Generation Sequencing Gene Panel in Providing Molecular Diagnosis to Patients With a Broad Spectrum of Neurodevelopmental Disorders.

Background: Neurodevelopmental disorders comprise a clinically and genetically heterogeneous group of conditions that affect 2%–5% of children and represents a public health challenge due to complexity of the etiology. Only few patients with unexplained syndromic and non-syndromic NDDs receive a diagnosis through first-tier genetic tests as array-CGH and the search for FMR1 CGG expansion. The aim of this study was to evaluate the clinical performance of a targeted next-generation sequencing (NGS) gene panel as a second-tier test in a group of undiagnosed patients with NDDs. Method: A 221-gene next-generation sequencing custom panel was designed and used to analyze a cohort of 338 patients with a broad spectrum of NDDs (202 males and 136 females) including Intellectual Disability (ID), Autism Spectrum Disorders (ASD), Epilepsy, language and motor disorders. Results: A molecular diagnosis was established in 71 patients (21%) and a de novo origin was present in 38 (64.4%) of the available trios. The diagnostic yield was significantly higher in females than in males (29.4% vs. 15.3%; p = 0.0019) in particular in ASD (36.8% vs. 7.6%; p = 0.0026) and Epilepsy (38.9% vs. 14.4% p = 0.001). The most involved genes were SLC2A1, SCN1A, ANKRD11, ATP1A2, CACNA1A, FOXP1, and GNAS altered in more than two patients and accounting for the 19.7% of the diagnosis. Conclusion: Our findings showed that this NGS panel represents a powerful and affordable clinical tool, significantly increasing the diagnostic yield in patients with different form of NDDs in a cost- and time-effective manner without the need of large investments in data storage and bioinformatic analysis.

Energy storage capacity optimization of wind-energy storage hybrid power plant based on dynamic control strategy

The construction of wind-energy storage hybrid power plants is critical to improving the efficiency of wind energy utilization and reducing the burden of wind power uncertainty on the electric power system. However, the overall benefits of wind-energy storage system (WESS) must be improved further. In this study, a dynamic control strategy based on the state of charge (SOC) for WESS is proposed to maintain a healthy SOC for energy storage system (ESS). Then, four scenarios with different operation strategies are set based on the historical operation data of a wind farm in China. Particle swarm optimization is used to solve the problem, with the goal of optimizing the overall system benefit. The results show that the dynamic control strategy is the most cost-effective while maintaining reliability. When compared to other scenarios, the profit of the dynamic control strategy is extended by 7.63 %, 327.69 % and 9.75 % respectively, and the energy storage life is extended by 10.02 %, 62.89 % and 21.61 % respectively, demonstrating that different working state of energy storage will significantly affect the storage life and, ultimately, affect the economic benefit of the system. Finally, the influences of feed-in tariff, frequency regulation mileage price and energy storage investment cost on the optimal energy storage capacity and the overall benefit of wind-energy storage hybrid power plant are discussed.

Carbon capture and storage investment strategy towards the dual carbon goals

This paper develops a real option model combined with the Monte Carlo simulation to analyze the impact of the “dual carbon goals” on the CCS investment timing in China. The trajectory of CO2 prices is predicted to follow a geometric Brownian motion with jump. The CO2 prices are predicted on the basis of three scenarios: high, medium, and low shocks, respectively. Furthermore, the real option method is adopted to derive the appropriate incentive schemes and corresponding target CO2 prices that sustain CCS operations to assist China’s dual carbon goals. Notably, portfolios of two government incentive policies are compared, including clean electricity tariff and carbon tax. The results show that building an efficient national carbon emissions trading scheme (ETS) is crucial to catalyzing the deployment of CCS technology, shortening the waiting time of CCS investment to 5.9 years at best. Government incentives could further bring the threshold CO2 prices down and the expected waiting time of CCS investment forward.

Computing necessary conditions for near-optimality in capacity expansion planning problems

In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supra-national level. However, their cost-optimal solutions are often not exploitable by decision-makers who are preferably looking for features of solutions that can accommodate their different requirements. This paper proposes a generic framework for addressing this problem. It is based on the concept of the epsilon-optimal feasible space of a given optimisation problem and the identification of necessary conditions over this space. This framework has been developed in a generic case, and an approach for solving this problem is subsequently described for a specific case where conditions are constrained sums of variables. The approach is tested on a case study about capacity expansion planning of the European electricity network to determine necessary conditions on the minimal investments in transmission, storage and generation capacity.

Revenue stacking for behind the meter battery storage in energy and ancillary services markets

Several sources of revenue are available for battery storage systems that can be stacked to further increase revenue. Typically, price arbitrage is used to gain revenue from battery storage. However, additional revenue can be gained from participation in ancillary services such as frequency response. This study presents a linear optimisation approach to account for local energy system participation in the wholesale day-ahead electricity market and multiple frequency response services. The methodology was applied to a school case study. A breakdown of market revenue and value of investment is presented for five operating strategies. The value of availability revenue and response energy revenue are distinguished for frequency response services. Finally, the impact of revenue stacking on battery degradation is assessed. The results show that local energy systems can decrease their operating costs and improve battery storage investment viability by stacking multiple revenues, whilst reducing degradation and increasing lifetime.

What adds more flexibility? An energy system analysis of storage, demand-side response, heating electrification, and distribution reinforcement

This paper compares various flexibility options to support renewable energy integration across the energy transition using energy system modelling. We analyse new flexibility assets such as electricity storage, heat pumps, demand-side response with existing wet appliances, electric boilers for domestic hot water and distribution grid expansion, along with energy efficiency measures in electrical appliances and building retrofitting. We propose an open-source sector coupling model (GRIMSEL-FLEX) to minimise, from a social planner perspective, the total cost of the energy system for electricity and residential heating supply in Switzerland, including various types of consumers and urban settings. We find relevant feedback mechanisms among various flexibility options. Firstly, electric boilers have a larger flexibility potential than demand-side response with wet appliances since they reduce storage investments by more than 26% by 2050 (only 12% for demand-side response). Secondly, 34% more electricity storage is needed if heat pumps replace all fossil-based heating and 80% to replace all heating systems entirely. Thirdly, we find a shift in the operation of heat pumps, electric boilers and wet appliances from night to midday, resulting in larger photovoltaic deployment (22%–66% for the residential sector). Finally, electricity storage capacity induced by heat pump deployment is highly dependent on the retrofitting rate. With 1% per annum, 86% of storage investments can be avoided and it can be counterbalanced with a high retrofitting rate of 2% per annum.

Investigating alternative power supply solutions under long term uncertainty for offgrid-offshore fish farm: The case of Hinnøya island, Norway

The aim of this paper is to explore potential least-cost decarbonisation solutions for an off-grid and offshore fish farm power supply system under long term uncertainty. The Hinnøya island in Norway is used as a use case. Three distinctive off-grid and grid-based alternative power supply solutions were proposed and studied as a replacement to the existing diesel power solution in a three-stage stochastic model and under two critical long-term uncertainties: (1) access to strong grid and (2) storage battery cost. The TIMES modelling framework is applied. The stochastic model results reveal that grid integrated storage is an optimal near-term investment for a storage battery cost of 295 €/kWh and less by 2025. In scenarios with no access to strong grid, grid integrated storage continues to be a least-cost solution in the long-term as well, whereas in those scenarios with strong grid access, new investment in storage is not required after 2030. Contrary to the stochastic model runs, the equivalent deterministic model runs showed that a hybrid wind and diesel solution is an optimal near-term investment. From 2030, however, a similar technology pattern in both stochastic and deterministic model runs are observed. Nevertheless, the results are very sensitive to the assumed storage battery costs. Higher storage costs (as high as 704 €/kWh by 2025) would make the hybrid wind and diesel solution an optimal solution instead of the grid integrated storage solution in near-term investment in both stochastic and deterministic model runs.

Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context – A model-based analysis of generation, network, and storage investments

In this paper, we explore centralized and more decentral approaches to succeed the energiewende in Germany, in the European context. We use the AnyMOD framework to model a future renewable-based European energy system, based on a techno-economic optimization, i.e. cost minimization with given demand, including both investment and the subsequent dispatch of capacity. The model includes 29 regions for European countries, and 38 NUTS-2 regions in Germany. First the entire energy system on the European level is optimized. Based on these results, the electricity system for the German regions is optimized to achieve great regional detail to analyse spatial effects. The model allows a comparison between a stylized central scenario with high amounts of wind offshore deployed, and a decentral scenario using mainly the existing grid, and thus relying more on local capacities. The results reveal that the cost for the second optimization of these two scenarios are about the same: The central scenario is characterized by network expansion in order to transport the electricity from the wind offshore sites, whereas the decentral scenario leads to more photovoltaic and battery deployment closer to the areas with a high demand for energy. A scenarios with higher energy efficiency and lower demand projections lead to a significant reduction of investment requirements, and to different localizations thereof.

Optimal configuration strategy of hybrid energy storage system on industrial load side based on frequency division algorithm

Electricity bills typically account for a large proportion of industrial users' production costs. Hybrid energy storage system (HESS), a high-performance energy storage method, has been widely used on the demand side. In the context of a two-part tariff system, the optimal configuration of battery-ultracapacitor HESS on the industrial load side realizes demand management and cost saving. Complete decoupling of load data promotes the full play to the advantages of batteries and ultracapacitors. Therefore, a VMD-ST-QF load frequency division strategy is proposed to decompose the chronological load curve into high-frequency and low-frequency components precisely. Depending on the results of frequency division, an optimal configuration strategy of HESS is established to minimize the net investment cost of energy storage. In this paper, the economic benefits of assembling energy storage on the industrial load side with four different configuration strategies are compared. The case study shows that the introduction of HESS based on the VMD-ST-QF strategy saves industrial users 5.26% of their total electricity cost and shortens the payback period of energy storage investment.

Impact on the power mix and economy of Japan under a 2050 carbon-neutral scenario: Analysis using the E3ME macro-econometric model

ABSTRACT This study uses the E3ME macro-econometric model to simulate what Japan’s macroeconomy would look like and how Japan's energy composition would change if the country were to achieve carbon neutrality by 2050. The results indicate that renewable energy will account for about 90% of the power supply configuration in 2050, assuming nuclear power plants are phased out by 2040. It is also predicted that GDP will increase by 4.0%–4.5% compared with the baseline scenario and that employment will improve by 1.5%–2.0%, resulting in simultaneous achievement of carbon neutrality and economic growth. The main reasons for these projected outcomes are that increased investment in renewable generation capacity in the power sector would be accompanied by increased investment in decarbonization technologies across individual economic sectors, increased private consumption resulting from increased employment and energy efficiency savings, and an improvement in the trade balance due to a substantial reduction in fossil fuel imports. In addition, the costs of energy due to policies to reduce or eliminate carbon would rise by 45%–55% point at most above the baseline scenario even in 2050; however, this would be of little burden on the economy, considering the substantial reduction in fossil fuel energy demand. Overall, it is estimated that energy bills would be 45% lower for consumers and 11% lower for industry in 2050 compared with the baseline. Key policy insights The study shows that achieving climate neutrality does not require difficult trade-offs with economic growth. Instead, it can provide economic opportunities that arise from switching to renewables and decarbonization technologies. The rapid reduction in global renewable costs for wind and solar technologies means that the net zero transition does not have to rely on relatively more expensive nuclear power. The true costs of nuclear could be a lot higher when the costs of safety regulations are properly accounted for. Instead, investment in electricity storage should be prioritized. A well balanced decarbonization policy mix for each sector is required. Policy makers should not rely on carbon pricing instruments alone. Supporting policies such as R&D spending, renewable subsidies, regulations and energy demand reduction should also be considered.

The Importance of Modeling Carbon Dioxide Transportation and Geologic Storage in Energy System Planning Tools

Energy system planning tools suggest that the cost and feasibility of climate-stabilizing energy transitions are sensitive to the cost of CO2 capture and storage processes (CCS), but the representation of CO2 transportation and geologic storage in these tools is often simple or non-existent. We develop the capability of producing dynamic-reservoir-simulation-based geologic CO2 storage supply curves with the Sequestration of CO2 Tool (SCO2T) and use it with the ReEDS electric sector planning model to investigate the effects of CO2 transportation and geologic storage representation on energy system planning tool results. We use a locational case study of the Electric Reliability Council of Texas (ERCOT) region. Our results suggest that the cost of geologic CO2 storage may be as low as $3/tCO2 and that site-level assumptions may affect this cost by several dollars per tonne. At the grid level, the cost of geologic CO2 storage has generally smaller effects compared to other assumptions (e.g., natural gas price), but small variations in this cost can change results (e.g., capacity deployment decisions) when policy renders CCS marginally competitive. The cost of CO2 transportation generally affects the location of geologic CO2 storage investment more than the quantity of CO2 captured or the location of electricity generation investment. We conclude with a few recommendations for future energy system researchers when modeling CCS. For example, assuming a cost for geologic CO2 storage (e.g., $5/tCO2) may be less consequential compared to assuming free storage by excluding it from the model.

Maximizing self-consumption rates and power quality towards two-stage evaluation for solar energy and shared energy storage empowered microgrids

Substantial penetration levels of intermittent and fluctuated renewable energy resources like PV can cause overcapacity and other operational challenges in the grid. Therefore, energy market actors are directed to the feed-in limitations and restrict the installed capacity. Likewise, the declining incentives make energy storage central to increasing self-consumption. However, economic uncertainties arise concerns about the financial feasibility of energy storage investments. This study presents the techno-economic benefits in increasing PV self-consumption using shared energy storage for a prosumer community under various penetration rates. In the first stage, the optimal energy storage allocations were done using the proposed New Best Algorithm and genetic algorithm with Matlab. Then, the technical performance of the proposed method was simulated with year-long using PSS Sincal. In the second stage, the economic feasibility of increasing PV self-consumption using shared energy storage under various penetration rates is evaluated considering residual energy. The effects of incentives are examined in terms of economic indicators such as payback period, net present value, and internal rate of return. The incentives promote prosumers either with or without energy storage to increase self-consumption. As a result, shared energy storage increased self-consumption up to 11% within the prosumer community. Results and sensitivity analysis are given in detail. The proposed method provides significant economic benefits and improved power quality.

Assessing the Socio‐Economic Effects of Carbon Capture, Utility and Storage Investment From the Perspective of Carbon Neutrality in China

AbstractCarbon Capture, Utility and Storage (CCUS) is essential for achieving carbon neutrality and has great development potential in China. CCUS, as a long‐term new investment, can address global warming and has significant social and economic impacts. This paper assessed the socio‐economic effects of CCUS investment based on carbon neutrality in China by combining the dynamic GTAP model and the Input‐output method. The result indicates that: CCUS investment may accumulate US$ 67.09 billion and US$ 776.61 billion from 2026 to 2030 and 2056 to 2060, respectively, based on International Energy Agency (IEA) investment scenario. Moreover, the corresponding value may increase to 8.26 billion and 743.21 billion US dollars from 2026 to 2030 and 2056 to 2060, respectively, based on Asian Development Bank (ADB) investment scenario; CCUS investment may stimulate gross value‐added of US$ 1.2 billion and US$ 10.4 billion based on ADB and IEA investment scenarios, respectively. CCUS investment may mainly promote the machinery industry, metal manufacturing industry, other service industries, power equipment manufacturing industry, and light processing manufacturing industry; Furthermore, ADB and IEA investment scenarios showed that CCUS industrial investment may indirectly create about 12,796 and 103,886 jobs, respectively, and US$ 85 million and US$ 692 million of labor employment income, respectively, in 2030. The corresponding values may increase to 0.82 million and 0.85 million jobs, respectively, and US$ 5,168 and US$ 5,396 million employment income, respectively, in 2060.

Carbon capture and storage investment: Fiddling while the planet burns

<h2>Summary</h2> Carbon capture and storage (CCS) has been recognized as a key technology in energy systems decarbonization. However, numerous attempts to deploy CCS failed, and the technology is still viewed as pre-commercial. Consequently, public investment in CCS has been largely limited to research, development, and demonstration (RD&D) in capture technology. While it is understood that private investment will typically focus on the development of intellectual property aimed at delivering a commercial advantage, there is a lack of evidence that public investment in CCS RD&D can deliver commercial viability. Here, we show that, while improved CCS technology in the electricity systems will deliver larger market shares to the technology developers, the benefit on overall system cost is negligible. Thus, public sector efforts should focus primarily on overcoming commercialization failures, such as the absence of CO₂ transport and storage infrastructures and other deployment barriers, leaving the development of intellectual property to the private sector.

Open source modelling of scenarios for a 100% renewable energy system in Barbados incorporating shore-to-ship power and electric vehicles

The high dependence on imported fuels and the potential for both climate change mitigation and economic diversification make Barbados' energy system particularly interesting for detailed transformation analysis. An open source energy system model is presented here for the analysis of a future Barbadian energy system. The model was applied in a scenario analysis, using a greenfield approach, to investigate cost-optimal and 100% renewable energy system configurations. Within the scenarios, the electrification of private passenger vehicles and cruise ships through shore-to-ship power supply was modelled to assess its impact on the energy system and the necessary investment in storage. Results show that for most scenarios of a system in 2030, a renewable energy share of over 80% is achieved in cost-optimal cases, even with a growing demand. The system's levelised costs of electricity range from 0.17 to 0.36 BBD/kWh in the cost-optimal scenarios and increase only moderately for 100% renewable systems. Under the reasonable assumption of decreasing photovoltaic investment costs, system costs of a 100% system may be lower than the current costs. The results show that pumped hydro-storage is a no-regret option for the Barbadian power system design. Overall, the results highlight the great potential of renewable energy as well as the technical and economic feasibility of a 100% renewable energy system for Barbados.

Cross-sector flexibility, storage investment and the integration of renewables: Capturing the impacts of grid tariffs

This article captures the role of electricity grid tariffs in the flexible electrification of district heating in the Nordic region at the horizon of 2050, in a situation of limited and optimal interconnections. Optimization of electricity and district heating systems is performed using the Balmorel energy systems model. Our results indicate that current volumetric tariffs substantially limit investment and flexibility from power-to-heat technologies and seasonal thermal storage in district heating. We also demonstrate how the additional flexible coupling of district heating enabled by adapted grid tariffs affects investment and generation in the electricity sector through a ratchet effect. We show that appropriate tariffs may result in up to 27% more electrification in district heating, 5% more wind and solar capacity investment in the electricity sector and up to 12% more wind and solar generation. Our results shed new light on the role of grid tariffs in the energy transition and inform policy-makers about the dynamics at play around tariff designs.

Improving the economics of fossil-free steelmaking via co-production of methanol

Steelmaking is responsible for 7% of the global net emissions of carbon dioxide and heavily reducing emissions from currently dominating steelmaking processes is difficult and costly. Recently, new steelmaking processes based on the reduction of iron ore with hydrogen (H2) produced via water electrolysis have been suggested. If the electricity input to such processes is fossil-free, near-zero carbon dioxide emissions steelmaking is achievable. However, the high electricity demand of electrolysis is a significant implementation barrier. A H2 storage may alleviate this via allowing a larger share of H2 to be produced at low electricity prices. However, accurately forecasting electricity market dynamics is challenging. This increases the risk of investment in a H2 storage. Here we evaluate a novel methanol-based H2 storage concept for H2-based steelmaking that also allows for the co-production of methanol. During electricity price peaks, the methanol can be reformed to produce H2 for the steelmaking process. During prolonged periods of low electricity prices, excess methanol can be produced and sold off, thus improving the prospects of storage profitability. We use historical electricity prices and a process model to evaluate methanol and fossil-free steel co-production schemes. Methanol co-production is found to have the potential to improve the economics of H2 supply to a fossil-free steelmaking process by up to an average of 0.40 €/kg H2 across considered scenarios, equivalent to a 25.0% reduction in H2 production electricity costs.

Assessments of linear power flow and transmission loss approximations in coordinated capacity expansion problems

With rising shares of renewables and the need to properly assess trade-offs between transmission, storage and sectoral integration as balancing options, building a bridge between energy system models and detailed power flow studies becomes increasingly important, but is computationally challenging.Here, we compare approximations for two nonlinear phenomena, power flow and transmission losses, in linear capacity expansion problems that co-optimise investments in generation, storage and transmission infrastructure. We evaluate different flow representations discussing differences in investment decisions, nodal prices, the deviation of optimised flows and losses from simulated AC power flows, and the computational performance. By using the open European power system model PyPSA-Eur, we obtain detailed and reproducible results aiming at facilitating the selection of a suitable power flow model.Given the differences in complexity, the optimal choice depends on the application, the user’s available computational resources, and the level of spatial detail considered. Although the commonly used transport model can already identify key features of a cost-efficient system while being quick to solve, severe deficiencies under high loading conditions arise due to the lack of a physical grid representation. Moreover, disregarding transmission losses overestimates optimal grid expansion by 20%. Adding a convex relaxation of quadratic losses with three tangents to the linearised power flow and accounting for changing line impedances as the network is reinforced suffices to represent power flows and losses adequately in design studies. We show that the obtained investment and dispatch decisions are then sufficiently physical to be used in more detailed nonlinear simulations of AC power flow evaluating their technical feasibility.