• Energy Research

The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country's scope, analyzing international interactions. In this study, we examine three Swiss energy policies, analyzing their impacts on both the national energy system and the cross-border electricity flows. We use a model inter-comparison approach with four electricity system models to explore scenarios involving Swiss renewable generation targets, the Swiss market integration, and the Swiss winter import limitations, in the context of various European electricity developments. The results indicate that a renewable generation target leads to a reduction in net imports and electricity prices. Additionally, reduced market integration impacts both Swiss and European energy transitions by limiting trade benefits, underutilizing Variable Renewable Energy Sources (VRES), and increasing electricity supply costs. Lastly, we observe that limiting Swiss winter imports adversely affects electricity trading, driving up both supply costs and electricity prices. The main body of the paper has 16 pages, 11 figures and 4 tables. The supplementary material has 7 pages, 6 figures and no tables

Spatially-disaggregated projections of new solar photovoltaic (PV) installations are essential for planning electricity grids and managing the electricity system at large scale. Such projections at sub-national level can be obtained by statistical models or by electricity system optimization models, but there is barely any study that compares the performances of these approaches. This study aims to compare methods for projecting PV installations at a level of 143 districts in Switzerland, using a simple extrapolation method (as a benchmark of the common practice today), a multiple linear regression model, two spatial regression models, and a spatially-explicit optimization model (EXPANSE) with various features to account for policy. The performance of different approaches is evaluated retrospectively for 2012–2020, using multiple accuracy indicators. The evaluation results show that statistical regression models, which account for socio-demographic and techno-economic characteristics as predictors of future PV growth, overall perform better than simple extrapolation or optimization. Although commonly used, extrapolation has the highest variability in accuracy, indicating the least robust performance. The optimization model tends to underestimate PV installations in its least-cost scenarios, if the role of policy is not considered. Incorporating solar PV policies and renewable electricity generation targets increases the overall accuracy of the optimization model at a national level, but not necessarily at a spatially-explicit level. We thus conclude that statistical models are preferred over extrapolation or optimization models for projecting future PV installations at a sub-national scale. ISSN:0360-5442 ISSN:1873-6785 Energy, 285

Weather resilience of the electricity system with high shares of Variable Renewable Energy Sources (VRES) could potentially be increased by spatially siting these sources in a way that dims the impact of weather. Here, we use single-year high-resolution modeling to test the resilience of the Swiss electricity system in 2035 under four siting strategies for new solar PV, wind power plants, and heat pumps: expected siting (continuation of the current spatial trends), even siting that is proportional to the technical potential or population, and the minimum system cost approach from the system's perspective. Using weather data from 1995 to 2019, we calculate nine electricity system resilience indicators for each siting strategy, accounting for diversification, decentralization, import dependency, load shedding, and curtailment. We find that a Swiss system in 2035 running fully or almost fully on VRES is resilient to historical weather variations. The four siting strategies perform relatively similarly in terms of resilience, indicating that VRES locations are neither a major concern nor a promising solution to influence weather resilience in a small country, like Switzerland. Having said that, minimum system cost approach that sites technologies in a cost-optimal way from the system's perspective has consistent, albeit minor, advantages for resilience, especially for minimizing load shedding and curtailment.

Energy projections are of great importance to energy policies but have consistently shown noticeable and repeated errors, and thus require accuracy assessment for improvement. International Energy Outlook (IEO) of the US Energy Information Administration and World Energy Outlook (WEO) of the International Energy Agency publish widely used energy projections, whose accuracy for China – the largest energy consuming economy and carbon dioxide emitter – has been rarely explored. This study investigates accuracy of China's reference energy projections in the annual reports of IEO and WEO from 2004 to 2019. Results show that most projections in IEO and WEO underestimated China's total energy consumption, particularly over longer projection horizons. The use of coal, natural gas and renewable energy tended to be underestimated, and nuclear energy was overestimated. The errors of industry and transport sectors were comparable and higher than for the other sectors. WEO showed substantially better accuracy than IEO in projections of total energy consumption, primary energy resources (except for nuclear energy) and end-use sectors. Projection horizon, errors in projected population's size, oil price and gross domestic product per capita were four leading factors related to the projection errors and hence they require particular attention in future modeling. For policy makers, this study shows that, if IEO and WEO projections are used to guide the policy making, China needs more aggressive policies in order to achieve the carbon neutrality goal.

Abstract Integrated assessment and energy system models are challenged to account for societal transformation dynamics to produce feasible low-carbon pathways. Yet, empirical evidence is lacking on which factors should be incorporated, how and to what extent this would improve the quality and relevance of modeled pathways. Here, we include six societal factors related to (i) infrastructure dynamics, (ii) actors and decision making and (iii) societal and institutional context into an open-source simulation model of the national power system transition. We apply this model for 31 European countries and, using hindcasting (1990–2019), quantify which societal factors improved the modeled pathways. We find that, if well-chosen and in most cases, incorporating societal factors can improve the hindcasting performance by up to 24% in terms of modelled installed capacity of individual technologies, but there are also situations where hindcasting performance can become worse. The combinations of most relevant societal factors differ among countries and model outputs, but infrastructure lock-in, public acceptance and investment risks contribute more strongly and frequently to model performance improvement. Our study hence paves the road to evidence-based choice of societal factors to be included in energy transition modeling in a systematic and transparent way.

This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into a two-stage unit commitment optimization. The planning objective consists in minimizing operating costs and/or equivalent carbon dioxide (CO2) emissions. Based on distributions of forecasting errors of the net demand, a LOLP-based risk assessment method is proposed to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction and the prescribed OR requirement. In a second stage, possible future forecasting uncertainties are considered. Hence, a stochastic operational planning is optimized in order to commit enough flexible generators to handle unexpected deviations from predic-tions. The proposed methodology is implemented for a local energy community. Results regarding the available operating reserve, operating costs and CO2 emissions are established and compared. About 15% of economic operating costs and environmental costs are saved, compared to a deterministic generation planning while ensuring the targeted security level.