Energy system optimization models are widely used to aid long-term investment decision-making for energy systems. From a socio-technical system viewpoint, existing models focus on the cost modeling of the technical subsystem, while the indirect costs of the social subsystem are not often modeled. This paper incorporates indirect costs into such a model, including those associated with generation capacity, energy production, and bilateral trades, respectively. As a proof-of-concept, the model has been applied to a case study for the Dutch power system, reflecting the Dutch national program Regional Energy Strategies, where regions collectively plan wind and solar energy capacities. We conclude that incorporating indirect costs significantly changed the optimal investment capacities and the associated costs for the regions compared to benchmark results from the conventional models. Furthermore, in this case study, a potential free-rider problem with regard to the national climate target occurs. Our model is used as a negotiation simulator to inform the regions about the hypothetical free-riding behaviors and thus helps to achieve a socially acceptable investment plan. The proposed energy system optimization model with indirect costs goes beyond the prevalent cost-minimization paradigm, and can be used to study transaction costs, trading barriers, and willingness to pay.@en

As the use of distributed energy resources increases, peer-to-peer (P2P) energy trading is becoming a promising way to harmonize the decarbonization and decentralization transformations in the energy sector. P2P markets give households the autonomy to make individual decisions and thus they may cooperate with each other to obtain economic benefits. However, existing studies on cooperative behaviors in P2P markets focus mostly on the electricity sector and P2P multi-energy markets are rarely studied. In fact, other energy carriers not only constitute a large part of the total energy demand, but their coupling can potentially benefit the system as well as the end-users. In this paper, we propose a P2P multi-energy market mechanism that allows peers to trade both electricity and heat. Two trading coalitions, i.e., an electricity-only trading coalition and an electricity–heat trading coalition, are predefined. The peers will join one of the coalitions based on their potential benefits and will trade energy inside the coalition. The energy markets are cleared separately per coalition and per energy carrier and hence, multi-energy markets are modeled. The proposed mechanism is a first-of-its-kind that explores the integrated effects of the multi-energy coupling and the cooperative behaviors in the P2P market. It is illustrated by a case study on a neighborhood in the Netherlands using realistic data. Results show that the mechanism is prosumer-centric as peers choose to join different coalitions at different time steps which benefit them the most. Compared to the reference scenario where there is no P2P trading, the P2P multi-energy market leads to higher economic benefits for all the peers altogether and benefits most individuals. The case study also demonstrates a benefit transfer from service-sector peers to residential peers.@en

The pledge for a carbon-free energy system in 2050 requires significant investments into renewable energy sources (RES). The relevant questions are: what technologies to select, where to build them, how much the capacities are, and at what cost. In order to answer these techno-economic questions, optimization models are commonly used to sketch a least-cost future energy system. However, the energy system is far more complex than a mathematical model. Although optimization models can provide the least-cost system design, they do not guarantee that we can realize this design because some key aspects are not captured by such models: the impact of public acceptance issues, conflicting interests among stakeholders, and the imperfection of markets. These non-technical aspects are generalized as institutions in this thesis. In a socio- technical system like the energy system, considering both the social aspects, the institutions, and the technical system, is pivotal. Therefore, the goal of this thesis is to improve optimization models by including institutions in energy system planning. Since institutions are not commonly mentioned in energy system planning models, this thesis starts with standardizing institutions, and we conducted a literature review. The goal is to provide a common ground for discussing institutions and find research trends and gaps in the state-of-the-art. We identified the following research gaps that need deliberate attention: spatial policies, collective decision-making, and bilateral trading with externalities. In this thesis, we developed three models to deal with these institutions. Since these institutions are indispensable in a socio-technical system, including them in optimization models results in socio-technically optimal future energy system designs beyond only the techno-economic optimums. @en

Variable Renewable Energy Sources (VRES) are characterized by intensive land-use and variable production. In existing optimization models that minimize the total cost of the energy system, location-specific VRES production profiles are often used to estimate VRES potential, but land-use and land cover aspects have been largely ignored. In this study, we therefore connect the literature in land cover assessment, VRES potential estimation and energy system optimization modelling by proposing a spatially explicit planning approach. This approach was applied to a case of the Netherlands to showcase its applicability and strength and to give results towards various RES targets. A baseline land-use scenario, a scenario with stricter constraints on land-use that reflects social resistance and spatial policy on wind energy and, thirdly, a scenario assuming unlimited land availability were analyzed. The baseline scenario results show the optimal geographical distribution of the generation capacities over the Netherlands. Wind energy dominates the generation mix and storage is only present at the 100% RES target. Under the strict constraints on land-use, 92% of the suitable land in the country will be deployed to place wind turbines in order to reach 100% RES share compared to 37% in the baseline case. However, the cost of electricity only increases by no more than 5 €/MWh. The unlimited land scenario highlights that the regional optimized capacities are infeasible. Apart from the useful results from the case study, the proposed approach is a first-of-a-kind contribution to the literature and provides a data-driven way to operationalize the location-specific land-use of VRES such that the role of the constraints on the land-use of VRES can be revealed and that policy-relevant results can be obtained.@en

This paper reviews the literature on the modeling approaches on decentralized energy investment and operation in the prosumer era. The study has several contributions. Firstly, it adds investment models into the review which have not been previously reviewed for decentralized energy modeling. Secondly, a modeling framework consisting of four building blocks is proposed that covers model conceptualization and model operationalization. Thirdly, the relationship between trading mechanisms and model methods is revealed using four evaluation criteria. Furthermore, by reviewing the papers, several trends in the literature are found. Operational models and local markets have been extensively studied, while wholesale market integration and investment models lack scientific attention. Among different trading mechanisms, the usage of bilateral contracts is most commonly seen. Lastly, optimization models significantly outnumber other model methods, and then it follows that their pitfalls such as the scalability of the model and the existence of stable outcomes need to be further addressed in future research.@en

Renewable energy investment is a complex process where multiple actors are often involved with their own, sometimes conflicting, interests. Here we propose a multi-actor multi-objective regional energy system planning approach to help actors gain mutual understanding regarding each other’s optimal investment wishes, in order to advance the planning process. This approach combines two models: Multi-Objective Optimization (MOO) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach uses illustrative objectives and actors which is then applied to the greater Amsterdam region to showcase its usage and strength. The four chosen objectives, i.e. total Capital Expenditure, total Operation & Maintenance costs, land-use and visually impacted area are minimized simultaneously to obtain a set of Pareto-optimal solutions. These solutions are then evaluated for governments, funders and local residents with different preferences using TOPSIS. The case study shows that our approach is unique and useful when multiple actors have to decide together upon the energy investment capacities. It is able to provide quantitative and optimal decision-aiding from the multi-actor perspective and generate also sub-optimal yet acceptable solutions for all the actors. Based on our approach, the impacts of policy options can be revealed from the actors’ perspectives as well.@en

Dutch regional municipalities increasingly take an active role in the transition to more sustainable and autonomous energy supply systems, using local energy sources like wind, solar and biomass. The ambition, on the one hand, concerns how an optimal local energy supply system can be designed such that local energy targets can be realized with minimum dependence on the national energy grids. On the other hand, it is of importance to consider the coordination mechanisms between actors such as municipalities, local communities and grid operators, since they will influence the technical configuration of the system. In the literature about renewables-based regional energy systems, the technical optimizations are done mostly from a central planner point of view. Therefore, there lacks a study on the optimization models for regional renewable energy planning that has a comprehensive view on coordination mechanisms and their influence on the system performance. The objective of this work is to enhance the formulation of for self-sufficient regional energy systems by taking coordination mechanisms into account, in order to understand their influences on the system performance. In this paper, a toy model for making optimal long-term investment decisions in electricity generation and transmission will be presented. Two coordination mechanisms, namely one with a central planner, and the other one with a regional market, are considered. In addition, the different modeling approaches for rural and urban energy systems will be discussed. Initial results show that the coordination with a central planner has the least system cost. In the market-based coordination, it is recognized that the degree of shared information and of market participation influences the problem formulation. This results in the cost differences for different coordination mechanisms and for different actors, and thus gives policy implications in the choice of coordination mechanisms and in cost allocation.@en