This paper reviews discounting approaches for modeling multi-year energy investments, focusing on total versus annualised cost formulations. We discuss how time value of money is handled, and how salvage value and milestone-year weighting can address mismatches between asset lifetimes and model horizons. These methods are implemented in the open-source TulipaEnergyModel to support transparent and tractable long-term energy system planning.

This paper reviews two established formulations for modelling multi-year energy investments: the simple method, which aggregates all capacity regardless of commissioning year, and the vintage method, which explicitly tracks investments by year to capture differences in technical parameters over time. While the vintage method improves modelling fidelity, it significantly increases model size. To address this, we propose a novel compact formulation that maintains the ability to represent year-specific characteristics while reducing the dimensionality of the model. The proposed compact formulation is implemented in the open-source model TulipaEnergyModel.jl and offers a tractable alternative for detailed long-term energy system planning.

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.

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.

Investments into renewable energy are increasing rapidly around the world. Energy system models are able to provide insights into optimal investment capacities and thus are widely used to aid the long-term investment decision-making under an electricity market environment. Existing energy system models, however, fail to consider bilateral electricity markets while in reality, these constitute a major part of all energy trades. In this paper, we propose an improved energy system model that endogenously considers mixed bilateral and pool markets. In this model, we also introduce three externality cost items that account for the social cost of technologies, carbon taxes/renewable energy subsidies, and the bilateral product differentiation in the bilateral market, respectively. We start with an equilibrium problem formulation for different market players and next, an equivalent optimization problem is presented. Then, a case study of the pan-European market to reach 95% emission reduction in 2050 is conducted to demonstrate the model. Different scenarios are constructed to showcase two different usages of product differentiation in the bilateral market, i.e., willingness to pay and exogenous costs. Our main conclusion is that the inclusion of mixed bilateral and pool markets into our enriched energy system model significantly changed the optimal investment capacities, compared to benchmark results from the existing, conventional energy system model. This shows that the inclusion of the bilateral market is of key importance in future investment considerations. Our model is the first of its kind to include this important and realistic bilateral market in energy system models.

We propose a framework where generation and transmission capacities are planned concurrently in market environments with a focus on the prosumers. This paper is a continuation of Part I and presents numerical results from three archetypal case studies. Following the proposed framework, optimal planning decisions are shown in all the cases. Furthermore, in case study I, no-regret planning decisions considering the uncertainties in future electricity market designs are discussed. In case study II, we look at a situation where the social resistance of wind energy prevails and the prosumers choose to not invest in wind energy. This preference results in an increased system cost, and thus may harm other prosumers. The framework is used as a negotiation simulator to deal with this problem. Case study III presents numerical results for a mixed bilateral/pool market. The case studies utilize realistic data from the Dutch power system and the European power system to provide policy-relevant results that aid their decarbonization in various market environments.

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.

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.

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 (sic)/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.

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.