Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into demand reduction or efficiency with high upfront costs as it cannot compute the benefits over the time span necessary for full cost recovery. These aspects should be considered when choosing the foresight horizon.

Distributed solar photovoltaic generation is less expensive than the retail price of electricity from the grid in most parts of Europe. Growing shares of variable generation place a focus on Renewable Energy Communities (REC) to increase the economic benefits of local energy systems. Civil society could play an influential and necessary role in the development of such communities, the expansion of renewable energy capacity and the provision of flexibility. However, current RECs models still confine tenants to their role as non-participating consumers. This article provides a concept to enable participative RECs within the German regulatory framework through collective self-consumption by including consumers for a fair allocation of renewable electricity using the blockchain technology.

Climate and energy policies are tools used to steer the development of a sustainable economy supplied by equally sustainable energy systems. End-users should plan their investments accounting for future policies, such as incentives for system-oriented consumption, emission prices and hydrogen economy, to ensure long-term competitiveness. In this work, the utilization of variable renewable energy and flexibility potentials in a case study of an aggregate industry is investigated. An energy concept considering PV and battery expansion, flexible production, fuel cell electric trucks (FCEV) and hydrogen production is proposed, and analysed under expected techno-economic conditions and policies of 2030 using an energy system optimization model. Under this concept, total costs and emissions are reduced by 14% and 70%, respectively, compared to the business-as-usual system. The main benefit of PV investment is the lowered electricity procurement. Flexibility from schedulable manufacturing and hydrogen production increases not only the self-consumption of PV generation from 51% to 80% but also the optimal PV capacity by 41%. Despite the expected cost reduction and efficiency improvement, FCEV is still not competitive to diesel trucks due to higher investment and fuel prices, i.e., its adoption increases the costs by 8%. However, this is resolved when hydrogen can be produced from own surplus electricity generation. Our findings reveal synergistic effects between different potentials and the importance of enabling local business models, e.g., regional hydrogen production and storage services. The SWOT analysis of the proposed concept shows that the pursuit of sustainability via new technologies entails new opportunities and risks. Lastly, end-users and policymakers are advised to plan their investments and supports towards integration of multiple application, consumption sectors and infrastructure.

For an organisation, one aspect on the path to a decarbonised future is the cost-optimal decarbonisation of their facilities’ energy systems. One method to guide the decarbonisation is internal carbon pricing. However, the design process of decarbonisation pathways, guided by internal carbon prices, can be challenging, since the energy system environment consists of many uncertainties. Despite the numerous uncertainties and existing methods to address uncertainties during the optimisation process, the optimisation of a facility’s energy system is often done by assuming perfect knowledge of all relevant input parameters (deterministic optimisation). Since real-world decisions can never be based on perfect knowledge and certain decisions might lead to path dependencies, it is important to consider the robustness of a solution in the context of developments that vary from the assumed scenarios. So far, no academic work has analysed the potential benefits of using an optimisation method that considers uncertainty about future CO₂ prices and energy carrier cost as two important input parameters during the optimisation process. This publication closes the knowledge gap by optimising a real-world energy system of a manufacturing site with two-stage stochastic programming and comparing it with methods of deterministic optimisation. The results show considerably more robust results for the solutions generated by stochastic programming. The total cost deviation does not exceed 52%, while the deviation of the deterministic results reaches up to 96%. The results also indicate that organisations should not analyse their energy systems by only considering uncertain internal carbon prices, but should examine the effects together with other important but uncertain parameters.