District heating networks (DHNs) are essential for decarbonizing building energy demand and are expected to play a larger role in future energy systems. Optimizing DHN design is vital as it directly impacts system cost and performance. Current optimization methods focus on minimizing cost without considering technical performance or its impact on its end-users. In some regions, there is limited social acceptance for DHN due to poor technical performance, highlighting the need to integrate consumer-oriented performance criteria in the design phase. This study proposes a DHN optimization method that explicitly incorporates user-level performance when evaluating different DHN designs. Two design strategies-a cost-optimal design and a maximum-efficiency design-are compared across 100 small, randomly generated DHNs and on a large real-world case. For small DHNs, the cost associated with efficiency improvements shows high variability. Only 6% of cases exhibit a cost increase below 10% per 1% efficiency gain, and only 3% are within the range of 0.5-2%. In the large DHN case, efficiency optimization increases the network’s efficiency from 56.5% to 69.7% at 18% cost increase. Efficiency-oriented designs significantly reduce consumer exposure to thermal discomfort under cold outdoor conditions or heat-source disturbances, and require less energy to meet demand. As network efficiency can potentially yield great benefits for consumers in the DHN, focusing solely on cost optimization is shortsighted. More emphasis on network efficiency may increase social acceptance of DHN and by that accelerate the energy transition.

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.

This paper develops theoretical foundations for extending Gauss-Hermite quadrature to robust design with computer experiments. When the proposed method is applied with m noise variables, the method requires 4m + 1 function evaluations. For situations in which the polynomial response is separable, this paper proves that the method gives exact transmitted variance if the response is a fourth-order separable polynomial response. It is also proven that the relative error mean and variance of the method decrease with the dimensionality m if the response is separable. To further assess the proposed method, a probability model based on the effect hierarchy principle is used to generate sets of polynomial response functions. For typical populations of problems, it is shown that the proposed method has less than 5% error in 90% of cases. Simulations of five engineering systems were developed and, given parametric alternatives within each case study, a total of 12 case studies were conducted. A comparison is made between the cumulative density function for the hierarchical probability models and a corresponding distribution function for case studies. The data from the case-based evaluations are generally consistent with the results from the model-based evaluation.

Flexibility coming from consumers in residential and service sectors has received significant attention to deal with uncertainty and variability of renewable energy sources. Since these consumers are too small individually to participate in the electricity markets, their assets can be pooled by an aggregator. The aggregator can implement business models by trading flexibility obtained from these consumers’ assets in different electricity markets. However, the aggregator and the consumers are only motivated to implement a business model, if it is economically feasible. The economic feasibility of a business model depends on (1) financial aspects: how much profit the aggregator makes, and how much money the consumers save, and (2) operational aspects: how the consumers’ assets are operated to increase the financial aspects. This paper aims to provide insights in these operational and financial aspects of the aggregator's business models in residential and service sectors. For this purpose, a literature review is conducted, and a framework is presented to analyze the selected papers on these operational and financial aspects. Based on this analysis, different strategies for the aggregator to implement business models are determined. Moreover, knowledge gaps are identified and several recommendations for future research are provided.

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.

Aggregators are considered essential to obtain flexibility from small residential and service sector consumers. They can implement business models by trading flexibility from their consumers' assets in various electricity markets. The aim of this paper is to identify challenges faced by aggregators with different roles, while implementing business models. We consider aggregators possessing three roles: of a supplier, Balance Responsible Party (BRP), and of an independent aggregator. The results show that challenges identified create higher complexity for aggregators with BRP's role and independent aggregators to implement business models, while it is significantly easier for aggregators with supplier's role. Recommendations are given to overcome the higher complexity: standardization of contracts and raising consumers awareness. These recommendations facilitate aggregators with different roles to implement their business models, and enable a healthy competition in electricity markets.

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.

Consumer batteries, when bundled by an aggregator, can provide flexibility by being used for consumers self-consumption and by offering Frequency Containment Reserve (FCR). Combining these two can also generate additional revenues for consumers since the aggregator pays an allowance to the consumers for FCR, called FCR price. The aim of this paper is determine the optimal share of consumer batteries that should be reserved for self-consumption and FCR to minimize the consumer's cost, while considering various FCR prices and investment cost of batteries which is paid by either consumers or the aggregator. For this purpose, an optimization model is presented, and applied to a case study in the Netherlands. The results show the consumers annual cost is minimized by finding the optimal FCR share of batteries. Despite that, investing in the battery is still not profitable for the consumers, unless the aggregator invests in the batteries.

The high level of uncertainty of renewable energy sources generation creates differences between electricity supply and demand, endangering the reliable operation of the power system. Demand response has gained significant attention as a means to cope with uncertainty of renewable energy sources. Demand response of residential and service sector consumers, when accumulated and managed by aggregators, can play a role in existing electricity markets. This paper addresses the question to what extent aggregator-mediated demand response can be used to deal with the impacts of the uncertainty of solar generation. Uncertain solar generation leads to imbalances of an aggregator. These imbalances can be reduced by shifting flexible loads, which is called demand response for internal balancing. The aim of this paper is to assess the impact of demand response from loads in residential and service sectors for internal balancing to reduce the imbalances of an aggregator, caused by uncertain solar generation. For this purpose, a Model Predictive Control model which minimizes the imbalances of the aggregator through load shifting is presented. The model is applied to a realistic case study in the Netherlands. The results show that demand response for internal balancing succeeds in reducing imbalances. Even though this is favorable from the power system's perspective, economic analysis shows that the aggregator is not financially incentivized to implement demand response for internal balancing.

Systems engineers are equipped to design complex networked systems such as infrastructures. A key goal is cost minimization over a vast solution space. However, finding a minimum-cost system while comprehensively satisfying different stakeholders is challenging and lacks proper methodological support. Stakeholders often employ their own expert estimations for lack of suitable decision-support methods. In these settings, systems engineers typically require mid-fidelity, easy-to-use methods. We present a rigorous method that quickly finds minimum-cost solutions for networks with multiple sources and sinks, focusing on pipeline topology, length, and capacity. It can serve as a discussion tool in multiactor design processes, to demarcate the design space, indicate sources of uncertainty, and provoke further analyses, different designs, or contractual negotiations. It is applicable to a wide variety of cases, including many prominent infrastructures needed to mitigate CO₂. We prove that the optimal layout is a minimum-cost Gilbert tree, and develop a heuristic based on the Gilbert-Melzak method. We demonstrate the method's efficacy for a case set regarding solution quality, computational time, and scalability. We also show its efficiency and usefulness for systems engineers in real-world settings. Systems engineers can use the generated cost-optimal system designs to benchmark any design changes in real-world negotiation processes.

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.

Aggregators are considered essential to extend demand response (DR) to small residential and service sector consumers. Both sectors currently have untapped load flexibility, which is considered key to support renewable resource integration. Aggregators can offer this flexibility in bulk to other power system parties. This paper addresses the question under which conditions DR can be profitable for both aggregators and end-consumers. The paper builds further on existing research that shows end-consumer preference for flat-rate tariffs. The aim is to find the range of flat-rate retail prices for different photovoltaic (PV) feed-in-Tariffs which make DR profitable for both aggregator and end-consumers. For this purpose, an optimisation model which minimises costs through load scheduling is presented. The model is applied using two approaches: optimising from aggregator's and from end-consumers' perspective. The results show that only the aggregator's perspective yields a range of flat-rate retail prices that are profitable for both actors. However, both the price range and the expected profits of DR are small.

This paper presents a systematic design analysis method based on the flexible design approach and the concept of real options to support decision-makers during conceptual design of infrastructure public–private partnership projects under uncertainty. It employs probabilistic and simulation methods to model uncertainty and flexible design concept to generate flexible design strategies within the physical layout and the contractual structure. Monte Carlo simulation is used to compare the value effects of design strategies. Illustrated on a stylized public–private partnership to develop a carbon capture and storage infrastructure, it was found that partners could
find design solutions that not only reduce risk exposure but also enable value-creation. For example, by designing the physical network with flexibility options such as extra capacity and length coupled with flexible revenue guarantee contract, partners can be able to reduce risk and enhance their respective value in the face of capacity demand uncertainty. Such a design strategy can be a promising way to realize multi-user carbon capture and storage investments.

Energy and industrial networks such as pipeline-based carbon capture and storage infrastructures and (bio)gas infrastructures are designed and developed in the presence of major uncertainties. Conventional design methods are based on deterministic forecasts of most likely scenarios and produce networks that are optimal under those scenarios. However, future design requirements and operational environments are uncertain and networks designed based on deterministic forecasts provide sub-optimal performance. This study introduces a method based on the flexible design approach and the concept of real options to deal with uncertainties during conceptual design of networks. The proposed method uses a graph theoretical network model and Monte Carlo simulations to explore candidate designs, and identify and integrate flexibility enablers to pro-actively deal with uncertainties. Applying the method on a hypothetical network, it is found that integrating flexibility enablers (real options) such as redundant capacity and length can help to enhance the long term performance of networks. When compared to deterministic rigid designs, the flexible design enables cost effective expansions as uncertainty unfolds in the future.

Network infrastructures, such as roads, pipelines or the power grid face a multitude of challenges, from organizational and use changes, to climate change and resource scarcity. These challenges require the adaptation of existing infrastructures or their complete new development. Traditionally, infrastructure planning and routing issues are solved through top-down optimization strategies such as mixed integer non linear programming or graph approaches, or through bottom up approaches such as particle swarm optimizations or ant colony optimizations. While some integrated approaches have been proposed int he literature, no direct comparison of the two approaches as applied to the same problem have been reported. Therefore, we implement two routing algorithms to connect a single source node to multiple consuming nodes in a topology with hard boundaries and no-go areas. We compare a geometric graph algorithm finding an (sub)optimum edge-weighted Steiner minimal tree with a Ant Colony Optimization algorithm implemented as an Agent Based Model. Experimenting with 100 randomly generated routing problems, we find that both algorithms perform surprisingly similar in terms of topology, cost and computational performance. We also discovered that by approaching the problem from both top-down and bottom-up perspective, we were able to enrich both algorithms in a co-evolutionary fashion. Our main findings are that the two algorithms, as currently implemented in our test environment hardly differ in the quality of solution and computational performance. There are however significant differences in ease of problem encoding and future extensibility.

With an increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which households manage their own energy generation and consumption, possibly in cooperation with each other. As a first step, in this paper a decentralized controller based on model predictive control is proposed. For an individual household using a micro combined heat and power (μCHP) plant in combination with heat and electricity storages the controller determines what the actions are that minimize the operational costs of fulfilling residential electricity and heat requirements subject to operational constraints. Simulation studies illustrate the performance of the proposed control scheme, which is substantially more cost effective compared with a control approach that does not include predictions on the system it controls.