Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system

Long-term expansion planning of the transmission network in India under multi-dimensional uncertainty

Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system

Corporate Social Responsibility and Sustainable Development Goal 9

With the spread of neoliberalism, corporate social responsibility (CSR) and private governance have become integral parts of corporate behavior. This entry discusses the aspects of Goal 9 (industry, innovation, and infrastructure) of the United Nations Sustainable Development Goals (SDGs) in relation to CSR. Goal 9 emphasizes sustainability, resilience, and equity of corporations, industries, and other social and economic actors in the processes of innovation and advancement of infrastructures. Although the concept of CSR, which represents positive social and environmental influences of corporations, is not explicitly mentioned in Goal 9, it is an important mechanism in accomplishing the objectives of the goal

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions

Modeling Storage and Demand Management in Electricity Distribution Grids

Storage devices and demand control may constitute beneficial tools to optimize electricity generation with a large share of intermittent resources through inter-temporal substitution of load. We quantify the related cost reductions in a simulation model of a simplified stylized medium-voltage grid (10kV) under uncertain demand and wind output. Benders Decomposition Method is applied to create a two-stage stochastic program. The model informs an optimal investment sizing decision as regards specific 'smart grid' applications such as storage facilities and meters enabling load control. Model results indicate that central storage facilities are a more promising option for generation cost reductions as compared to demand management. Grid extensions are not appropriate in any of our scenarios. A sensitivity analysis is applied with respect to the market penetration of uncoordinated Plug-In Electric Vehicles which are found to strongly encourage investment into load control equipment for 'smart' charging and slightly improve the case for central storage devices

Energy services

Energy consumers are driven by their demand for energy services (such as space and water heating, cooking, transportation, lighting, entertainment and computing). This piece introduces the reader to the concept of energy services, and explains why it is important to analyze energy markets and climate policies from the perspective of energy services. The paper discusses the theoretical foundations and empirical evidence, particularly related to the rebound effect and the demand in developing economies. The paper concludes that governments should encourage the collection of statistical information about energy services in order to help economists analyse markets and policies through this lens. Most importantly, governments should formulate more integrated policies that focus explicitly on energy services, connecting markets for energy and for energy-using equipment with the development of technologies. Careful and balanced energy service policies are especially important as economies industrialise because they can help reduce economic, political and environmental vulnerability