• Energy Research

The electricity landscape is constantly evolving, with intermittent and distributed electricity supply causing increased variability and uncertainty. The growth in electric vehicles, and electrification on the demand side, further intensifies this issue. Managing the increasing volatility and uncertainty is of critical importance to secure and minimize costs for the energy supply. Smart neighborhoods offer a promising solution to locally manage the supply and demand of energy, which can ultimately lead to cost savings while addressing intermittency features. This study assesses the impact of different electric vehicle charging strategies on smart grid energy costs, specifically accounting for battery degradation due to cycle depths, state of charge, and uncertainties in charging demand and electricity prices. Employing a comprehensive evaluation framework, the research assesses the impacts of different charging strategies on operational costs and battery degradation. Multi-stage stochastic programming is applied to account for uncertainties in electricity prices and electric vehicle charging demand. The findings demonstrate that smart charging can significantly reduce expected energy costs, achieving a 10% cost decrease and reducing battery degradation by up to 30%. We observe that the additional cost reductions from allowing Vehicle-to-Grid supply compared to smart charging are small. Using the additional flexibility aggravates degradation, which reduces the total cost benefits. This means that most benefits are obtainable just by optimized the timing of the charging itself.

Abstract District heating is an under-researched part of the energy system, notwithstanding its enormous potential to contribute to Greenhouse Gas emission reductions. Low-temperature district heating is a key technology for energy-efficient urban heat supply as it supports an efficient utilization of low-grade waste-heat and renewable heat sources. The low operating temperature for such grids facilitates the integration of seasonal thermal energy storage, enabling a high degree of operational flexibility in the utilization of both uncontrollable and controllable heat sources. Yet, an inherent challenge of optimizing the operation of low-temperature district heating networks and its flexibility is the underlying uncertainty in heat demand. We develop a new stochastic model to minimize the total operational cost of district heating networks with local waste heat utilization, seasonal storage and uncertain demand. We consider in particular how demand side management and seasonal storage can improve the operational flexibility and thereby reduce costs. We analyze different set-ups of a local low-temperature district heating network under development in a new residential area in Trondheim, Norway. We find up to 37% reductions in carbon dioxide emissions, 29% generation reduction in peak hours, and 10% lower operational costs. These large values highlight the significance of flexibility options in low-temperature district heating networks for cost-effective, large-scale deployment.

In this paper, we use the Global Gas Model to analyze the perspectives and infrastructure needs of the European natural gas market until 2050. Three pathways of natural gas consumption in a future low-carbon energy system in Europe are envisaged: i) a decreasing natural gas consumption, along the results of the PRIMES model for the EMF decarbonization scenarios; ii) a moderate increase of natural gas consumption, along the lines of the IEA (2012) World Energy Outlook's New Policy Scenario; and iii) a temporary increase of natural gas use as a bridge technology, followed by a strong decrease after 2030. Our results show that import infrastructure and intra-European transit capacity currently in place or under construction are largely sufficient to accommodate the import needs of the EMF decarbonization scenarios, despite the reduction of domestic production and the increase of import dependency. However, due to strong demand in Asia which draws LNG and imports from Russia, Europe has to increasingly rely on pipeline exports from Africa and the Caspian region from where new pipelines are built. Moreover, pipeline investments open up new import and transit paths, including reverse flow capacity, which improves the diversification of supplies. In the high gas consumption scenario similar pipeline links are realized-though on a larger scale, doubling the costs of infrastructure expansion. In the bridge technology scenario, the utilization rates of (idle) LNG import capacity can be increased for the short period of temporary strong natural gas demand.

We investigate the hydropower scheduling problem, in which a price-taking producer determines a reservoir management strategy that maximises the present value of revenues from selling the produced electricity in a well-functioning market. Uncertainty is present both in market prices and in reservoir inflows. To solve the problem, we apply linear decision rules, which is an approximation method for solving multistage stochastic linear programming problems. Traditional methods for solving these types of problems suffer from computational efforts that grow exponentially with the number of stages and state variables. By restricting the decision variables to be affine functions of the realisations of the uncertain parameters, the original intractable problem is transformed into a problem with short computational time. The aim is to investigate feasibility of the framework. The approach is demonstrated on four Norwegian hydropower plants using recent inflow and price data over a ten year time horizon. We obtain flexible reservoir management strategies, providing feasible solutions where a deterministic approach fails, and otherwise improving expected profits by up to 4.5% compared to a deterministic approach. Solutions times are in the order of minutes.

Assessing and quantifying the impacts of technological, economic, and policy shifts in the global energy system requires large-scale numerical models. We propose a dynamic multi-fuel market equilibrium model that combines endogenous fuel substitution within demand sectors and in power generation, detailed infrastructure capacity constraints and investment, as well as strategic behaviour and market power aspects by suppliers in a unified framework. This model is the first of its kind in which market power is exerted across several fuels. Using a dataset based on the IEA World Energy Outlook 2013 (New Policies scenario, time horizon 2010-2050, 30 regions, 10 fuels), we illustrate the functionality of the model in two scenarios: a reduction of shale gas availability in the US relative to current projections leads to an even stronger increase of power generation from natural gas in the European Union relative to the base case; this is due to a shift in global fossil fuel trade. In the second scenario, a tightening of the EU ETS emission cap by 80% in 2050 combined with a stronger bio-fuel mandate spawns a renaissance of nuclear power after 2030 and a strong electrification of the transportation sector. We observe carbon leakage rates from the unilateral mitigation effort of 60-70%.

Abstract In the energy strategy of the European Union, the end-user is envisioned as a key participant in the future electricity market (European Commission, [16] ). Current market designs and business models lack incentives and opportunities for regular electricity consumers (e.g. residential buildings) to become prosumers and actively participate in the market. Incentives should include economic and behavioural motivation beyond subsidised flat feed-in tariffs. Opportunities should allow for active participation of prosumers with relatively modest generation volumes but significant flexibility. In this paper, we propose a framework to integrate prosumer communities into the existing day-ahead and intraday markets. Using a two-stage stochastic programming approach, we incorporate the sequenced decision-making in the wholesale system under uncertainty of renewable generation and spot prices. We focus on the value of peer-to-peer (P2P) trading in the integration of prosumers in the day-ahead and intraday markets and investigate how residential battery storage contributes to local demand side flexibility in an integrated market setting. To this end, we introduce the Smart elecTricity Exchange Platform (STEP) that represents the interface between the wholesale electricity markets and the prosumer communities, and coordinates the community’s operational supply-demand decisions. A study on residential buildings in London show that both P2P trade and battery storage by themselves each induce a reduction of electricity bills by 20%–30%. Combined, P2P trade and battery storage may lead to savings of almost 60%. In other words, we find that peer-to-peer trade and flexibility options such as local storage generate higher levels of the community’s self-sufficiency.

This study investigates the role of battery electric vehicles in a future carbon-neutral European energy system, focusing on different vehicle charging strategies and their effects on the power system. Using an integrated planning model, the study analyzes the interaction between battery electric vehicle charging strategies and other flexibility options within the energy system. It compares three charging strategies – passive charging, flexible charging, and bi-directional Vehicle-to-Grid charging – which provide increasing levels of flexibility. The findings emphasize the cost-saving potential of flexible charging strategies. Flexible charging can reduce overall system costs by 7%, with the most significant savings and shifts seen in power generation. There is reduced investment in wind power, whereas investment in photovoltaic (solar) systems increases significantly compared to passive charging. Flexible charging improves solar energy's competitiveness over wind power by allowing peak solar generation to be absorbed as it occurs. There are also notable savings in fast-response options such as gas turbines, with 75% lower capacity investment in the scenario with the most flexible charging. In addition, investment in Power-to-X technologies, especially electrolyzers, is also lower due to more effective load shifting. Although flexible charging strategies may lead to challenging load peaks in the distribution grid, the study indicates that limiting the increase in load peaks to 60% can still result in a 6% reduction in overall system costs compared to passive charging. Our study results underpin that strategic charging management can facilitate the integration of renewable energy while lowering costs. Energy Reports, 13 ISSN:2352-4847

In this paper, we analyze market equilibrium models with random aspects that lead to stochastic complementarity problems. While the models presented depict energy markets, the results are believed to be applicable to more general stochastic complementarity problems. The contribution is the development of new heuristic, scenario reduction approaches that iteratively work towards solving the full, extensive form, stochastic market model. The methods are tested on three representative models and supporting numerical results are provided as well as derived mathematical bounds.