• Energy Research

Abstract Local electricity markets based on peer-to-peer (P2P) trading schemes have emerged as an innovative mechanism to sell electricity from prosumer to consumer, to utilise efficiently and value local flexibility, and to support grid management. In this paper, we analyse a local market applied to a real-life neighbourhood of 52 households in Norway. As prosumers and consumers trade within this community, we analyse the value of P2P trading compared to cases where no local markets are available, along with the impact of PV, batteries and EVs deployment. As these technologies and local trading interactions might create challenges to the physical operations of the grid, we analyse the effect on power flows, voltage variations and system losses. The main findings indicate that there are no significant impacts on the grid operation of the P2P market when only PVs are installed in the system. With decentralised batteries available, the P2P trade induced more voltage fluctuations and 14 % more losses within the neighbourhood than the case with no local market. However, the local market brings overall savings for the end-user and sets the frame to design pricing schemes (e.g. manage losses) that are tailored to support DSO operations.

Abstract Achieving the energy-related and environmental targets for nations and municipalities is largely dependent on the existing built stock. It plays a pivotal role in the accomplishment of these targets through the implementation of energy efficiency and flexibility programs, involving the deployment of distributed energy resource management technologies, refurbishment of building envelopes and upgrading of indoor environmental control equipment. Spatial awareness about urban energy use enables to prioritise the areas where these solutions will be most effective and balanced with the plans for new constructions. Large-scale building energy mapping, however, must cope with heterogeneity of buildings within the built stock, absence of detailed information and multiple sources of uncertainty that stem from the complex and dynamic properties of the phenomenon at a building level. One of the key challenges in the discipline is to account for these uncertainties while maintaining the rational model complexities and data needs. This study, therefore, suggests a parsimonious top-down probabilistic modelling recipe to enable geospatial energy mapping and analysis. Under such modelling principles, an inverse propagation of uncertainties is carried out from the status quo of the built stock. The proposed framework is based on probabilistic sampling with prior parametric univariate density estimation and statistical hypothesis testing. Consolidation with the exogenous influencing factors is facilitated through the measure of statistically significant difference. This approach is exemplified with the data from two sources: the cadastral system and the energy performance certificates registry. A case study developed for Trondheim (Norway) quantified the central tendency and dispersion in the distributions of the simulated bulk total annual energy use by buildings per 1 × 1 km grid cell over the urban territory. The results suggest that best estimates of these values vary between 11 MWh · y - 1 and 141 GWh · y - 1 depending on the grid cell. A measure of dispersion in the simulated results is highly correlated with these estimates. Robust handling of uncertainties and the possibility to accommodate a variety of modelling objectives make this approach practical for energy mapping with a flexible spatial resolution that may facilitate numerous applications in energy planning. A collection of methods for univariate density estimation discussed in this study together with the empirical data are accessible through Built Stock Explorer: https://builtstockexplorer.indecol.ntnu.no . This open web application for knowledge discovery in building energy data enables to reproduce some of the results presented in the article.

The intermittent nature of distributed energy resources introduces new degrees of uncertainty in the operation of energy systems; hence, short-term decisions can no longer be considered fully deterministic. In this article, an energy management system (EMS) was proposed to optimize the market participation and the real-time operation of a virtual power plant (VPP) composed of photovoltaic generators, non-flexible loads, and storage systems (e-vehicle, stationary battery, and thermal storage). The market bidding process was optimized through a two-stage stochastic formulation, which considered the day-ahead forecast uncertainty to minimize the energy cost and make available reserve margins in the ancillary service market. The real-time management of regulating resources was obtained through an innovative rolling horizon stochastic programming model, taking into account the effects of short-term uncertainties. Numerical simulations were carried out to demonstrate the effectiveness of the proposed EMS. The architecture proved to be effective in managing several distributed resources, enabling the provision of ancillary services to the power system. In particular, the model developed allowed an increase in the VPP's profits of up to 11% and a reduction in the energy imbalance of 25.1% compared to a deterministic optimization.

Hydrogen is seen as a key energy carrier to reduce CO2 emissions. Two main production options for hydrogen with low CO2 intensity are water electrolysis and natural gas reforming with Carbon Capture and Storage, known as green and blue hydrogen. Northern Norway has a surplus of renewable energy and natural gas availability from the Barents Sea, which can be used to produce hydrogen. However, exports are challenging due to the large distances to markets and lack of energy infrastructure. This study explores the profitability of hydrogen exports from this Arctic region. It considers necessary investments in hydrogen technology and capacity expansions of wind farms and the power grid. Various scenarios are investigated with different assumptions for investment decisions. The critical question is how exogenous factors shape future regional hydrogen production and export. The results show that production for global export may be profitable above 90 €/MWh, excluding costs for storage and transport, with blue hydrogen being cheaper than green. Depending on the assumptions, a combination of liquid hydrogen and ammonia export might be optimal for seaborne transport. Exports to Sweden can be profitable at prices above 60 €/MWh, transported by pipelines. Expanding power generation capacity can be crucial, and electricity and hydrogen exports are unlikely to co-exist. Developing hydrogen energy hubs: The role of H2 prices, wind power and infrastructure investments in Northern Norway

This paper concerns the development of a cost-effective approach to deploy the flexibility that electrical battery storage can offer to support the secure operation of medium voltage electrical networks under varying operating conditions. To this aim, and unlike related studies of the current state-of-the-art, the approach presented in this paper is based on a multiperiod optimization model that simulates half-hourly operational decisions. The optimization model includes a non-linear power flow equation based representation of the system, branch capacity constraints (instead of energy flows - the traditional state-of-the-art practice), different feasible battery sizes (currently available in the market), high RES penetration levels, the time of use electricity prices (half-hour dynamic prices), load data of customers (defined from real-world profiles), and reference battery costs (from existing literature). The objective is to decide the location and size of the battery storage within the medium voltage electrical network by minimizing the difference between operational and capital expenditures. The location of the battery storage is modeled with binary variables, whereas the size is modeled as a parameter first and then as a continuous variable. The value of the optimization model is demonstrated in a case study built upon a modified version of the IEEE 33 bus test system.

Energy and power system models represent important insights on the technical operations of energy technologies that supply the energy consumption in time steps with hourly resolution. This paper presents the European Model for Power system Investments with Renewable Energy (EMPIRE) that combines short-term operations with the representation of long-term planning decisions including infrastructure expansion. The EMPIRE model has an unique mathematical modelling structure based on multi-horizon stochastic programming, which means investment decisions are subject to short-term uncertainty represented by different realizations of operational scenarios. The model is open source and ready to use to analyse energy transition scenarios towards 2050 and beyond. This paper outlines the building blocks of the model and its software structure. We also present an illustrative example of results from using the software.

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.