Energy System Optimization Model Research Articles

Solving a large energy system optimization model using an open-source solver

Open-source energy models are becoming more widely used for electric power systems planning. The solutions for these models are often computed using commercial optimization solvers, which require licensing fees that can be a potential barrier for certain organizations and researchers. This study explores the ability of the open-source COIN-OR linear programming (CLP) solver to compute solutions for the Regional Energy Deployment System (ReEDS) model—a large-scale, open-access electricity system planning model for the United States developed by the National Renewable Energy Laboratory (NREL). We find that open-source solvers, such as CLP, require some reduction of model size and detail. Although the solutions for reduced-form models may differ from full-featured models, we demonstrate that reduced-form solutions for ReEDS can still provide useful insight about drivers of power sector evolution. This research can help the modeling community better understand how open-source solvers can be applied to large-scale planning tools, and the potential steps that may be required to implement them.

Optimization of integrated energy system for combined cooling, heating and power supply of new energy based on energy storage

With the development of science and technology, people also pay more and more attention to the development of new energy. Although there are also many studies on integrated energy systems now, integrated energy systems containing energy storage should also be further studied. This paper proposes an optimization of integrated energy system for combined cooling, heating and power supply of new energy based on energy storage, which analyzes the gas turbine, absorption refrigerating machine, electric refrigerator, photovoltaic power generation units, wind turbine and the work characteristics of the energy storage device. In this paper, an integrated energy system optimization model of new energy cogeneration with energy storage equipment is established. An example shows that the integrated energy system with energy storage can effectively solve the independent decoupling operation relationship among cool, heat and electricity. At the same time, the proposed model can also solve the energy interaction among cool, heat and electricity. In this way, the optimal operation of the integrated energy system can be realized.

Adequacy aware long-term energy-system optimization models considering stochastic peak demand

An important aspect of long-term power system planning models is their adequacy awareness, i.e., their ability to ensure generation adequacy in the final solution, which in turn strongly depends on the level of temporal detail included in the model structure. To maintain computational tractability, the temporal detail included in long-term planning models is, however, often limited, which decreases their adequacy awareness resulting in inadequate capacity mixes. To compensate for this, several adequacy-improving measures can be taken. The aim of this paper is to investigate the performance of three of these measures, namely (i) adding a traditional static planning reserve margin (PRM) constraint, (ii) adding a dynamic PRM constraint and (iii) a novel approach that increases the level of temporal detail with which critical peak periods are modeled. To this end, we compare each of these three adequacy methods in a long-term planning exercise based on (i) total system costs, (ii) adequacy of the resulting capacity mix and (iii) technology choices. Our results suggest that the novel approach more effectively increases the adequacy of the obtained capacity mix, resulting in lower total system costs and less technology biases. Also the dynamic PRM approach performs well, although occasional inadequacies can occur in individual milestone years due to exogenously imposed capacity credits.

Do multi-sector energy system optimization models need hourly temporal resolution? A case study with an investment and dispatch model applied to France

Energy system optimization models (ESOMs) increasingly cover the main energy-consuming sectors rather than just electricity, which massively raises calculation time. To reduce the latter, researchers apply various time-series aggregation methods, the pros and cons of which have been analyzed for electricity-only ESOMs but not for ESOMs also covering the main energy-consuming sectors. To address this question we compare the two main time-series aggregation methods: (1) reducing the temporal resolution (from one to two, four or eight hours) and (2) selecting representative periods (one week over one, two or three months, with an hourly resolution). We apply these methods to EOLES_mv, an ESOM covering the main French energy sectors. Both methods cut the calculation time by a similar amount but the former generates much smaller discrepancies for the main output variables (energy mix, system cost and CO2 emissions). These results are at odds with those generally obtained with electricity-only ESOMs, for which reducing the temporal resolution generates significant discrepancies when wind and solar dominate the electricity mix.

A column generation algorithm for solving energy system planning problems

Energy system optimization models are typically large models which combine sub-models which range from linear to very nonlinear. Column generation (CG) is a classical tool to generate feasible solutions of sub-models, defining columns of global master problems, which are used to steer the search for a global solution. In this paper, we present a new inner approximation method for solving energy system MINLP models. The approach is based on combining CG and the Frank Wolfe algorithm for generating an inner approximation of a convex relaxation and a primal heuristic for computing solution candidates. The features of this approach are: (i) no global branch-and-bound tree is used, (ii) sub-problems can be solved in parallel to generate columns, which do not have to be optimal, nor become available at the same time to synchronize the solution, (iii) an arbitrary solver can be used to solve sub-models, (iv) the approach (and the implementation) is generic and can be used to solve other nonconvex MINLP models. We perform experiments with decentralized energy supply system models with more than 3000 variables. The numerical results show that the new decomposition method is able to compute high-quality solutions and has the potential to outperform state-of-the-art MINLP solvers.

Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models

Energy system models are challenged by the need for high temporal and spatial resolutions in order to appropriately depict the increasing share of intermittent renewable energy sources, storage technologies, and the growing interconnectivity across energy sectors.This study compares different temporal aggregation strategies, which reduce the number of considered time steps, to maintain computational viability of these models. The work focuses on the representation of time series by a subset of single time steps (i.e., typical time steps), or by groups of consecutive time steps (i.e., typical periods), which are commonly applied in the literature using clustering. We test these techniques for two different energy system models and benchmark the optimization results based on aggregation to those of the fully resolved models. Further, centroids and medoids are used to represent the clustered datasets and it is investigated whether the optimal aggregation method can be determined based on clustering indicators only.The results reveal that typical time steps consistently outperform typical periods with respect to clustering indicators, but do not lead to more accurate optimization results when applied to a model that takes numerous storage technologies into account. Although both aggregation techniques are capable of coupling the aggregated time steps, typical periods offer more options to depict storage operations, whereas typical time steps are more effective for models that neglect time-linking constraints. Further, this observation is independent from the choice of centroids or medoids to represent the clustered time series.In summary, the adequate choice of aggregation methods strongly depends on the mathematical structure of the considered energy system optimization model, and a priori decisions of a sufficient temporal aggregation are only possible with good knowledge of the mathematical structure of the underlying optimization problem.

An energy system optimization model accounting for the interrelations of multiple stochastic energy prices.

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein–Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-021-04229-3.

Multi-objective optimization model for fuel cell-based poly-generation energy systems

Although the layout of combined cooling, heat and power (CCHP) systems is well known from a technical perspective, the optimal operating strategy and sizing of such systems are relatively complex, due to the high number of variables and constraints involved. To address this research gap, the present paper describes the formalization, implementation, and validation of an innovative multi-objective optimization model for CCHP poly-generative energy systems. As a novelty, the model uses two levels of optimization: a first level to optimize the best operating strategy of the system for different unit sizes, minimizing an economic cost function, maximizing the performance of the plant, and minimizing polluting emissions; the second level to optimize the size of the plant, through a technical-economic optimization function based on the Net Present Value. The model is highly flexible, allowing the execution of sensitivity analyses. The analyzed energy system is composed of a cogeneration unit, namely solid oxide and polymer electrolyte membrane fuel cells, and of heat pumps, electrical storage, and traditional auxiliary boilers. In order to prove the reliability of the CCHP units, the modeled performance curves for the fuel cell systems have been validated with existing commercial units, resulting in a degree of correlation mostly over 90%.

Strategic policy targets and the contribution of hydrogen in a 100% renewable European power system

The goal of the European energy policy is to achieve climate neutrality. The long-term energy strategies of various European countries include additional targets such as the diversification of energy sources, maintenance of security of supply, and reduction of import dependency. When optimizing energy systems, these strategic policy targets are often only considered in a rudimentary manner and thus, the understanding of the corresponding interdependencies is lacking. Moreover, hydrogen is considered as a key component of a fully decarbonized energy system, but its role in the power sector remains unclear due to the low round-trip efficiencies.This study reveals how fully decarbonized European power systems can benefit from hydrogen in terms of overall system costs and the achievement of strategic policy targets. We analyzed a broad spectrum of scenarios using an energy system optimization model and varied model constraints that reflect strategic policy targets. Our results are threefold. First, compared to power systems without hydrogen, systems using hydrogen realize savings of 14–16% in terms of the total system costs. Second, the implementation of a hydrogen infrastructure reduces the number of infeasible scenarios when structural policy targets are considered within the power system. Third, the role of hydrogen is highly diverse at a national level. Particularly, in countries with low renewable energy potential, hydrogen plays a crucial role. Here, high levels of self-sufficiency and security of supply are achieved by deploying hydrogen-based power generation of up to 46% of their annual electricity demand, realized via imports of green hydrogen.

Integration of groundwater heat pumps into energy system optimization models

Heat pumps are one of the key technologies for the mitigation of carbon emissions in the heating sector. Therefore, they are an essential modelling component when analyzing future energy systems. The focus of this work are groundwater heat pumps and their integration into energy system optimization models. Three different integration approaches are introduced and compared to each other. The approaches differ in the representation of heat pumps efficiency: constant, time dependent and both time and spatially dependent. The comparison of the proposed approaches is conducted using an energy system model of the residential heating sector in Munich and two optimization scenarios: 70 % and 95 % emission reduction compared to 2014. Assuming constant efficiency of heat pumps throughout the year produces incorrect optimization results, whereas adding spatial component to the temporal one does not change cumulative results significantly. Thus, the second approach is the most suitable one due to its lower complexity compared to the last approach. However, if spatially distributed results are required, then the last approach is necessary. Finally, these integration approaches are applicable to all types of heat pumps.

Long term storage in generation expansion planning models with a reduced temporal scope

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full-year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models.

Environmental and techno-economic assessment of power system expansion for projected demand levels in Kenya using TIMES modeling framework

This study develops a new national-scale bottom-up energy system optimization model called Kenya-TIMES. The model evaluates the implication of greenhouse gas emission's reduction on the techno-economic and environmental evolution of Kenya's power system under three government-projected electricity demand levels, which covers the 2020–2045 period. To assess the implications of greenhouse gas emission reduction measures, a business as usual and a carbon emission cap scenarios were developed. The model shows that energy security can be achieved under the two scenarios, and for all the three demand levels. The generation mix suggested by the model is dominated by renewable sources under the carbon emission cap scenario compared to the business as usual scenario. The higher share of renewable technologies under the carbon emission cap scenario results in lower emission but increased electricity cost. Consequently, to meet its emission reduction targets, the Kenyan government need to enact and implement policies that will enhance deployment of renewable energy technologies. The findings indicate that the Kenyan government should prioritize developing geothermal and hydropower resources in the short- to medium-term, which can provide affordable and secure energy while limiting GHG emissions.

The feasibility of energy autonomy for municipalities: local energy system optimisation and upscaling with cluster and regression analyses

The sheer number of alternative technologies and measures make the optimal planning of energy system transformations highly complex, requiring decision support from mathematical optimisation models. Due to the high computational expenses of these models, only individual case studies are usually examined. In this article, the approach from the author’s PhD thesis to transfer the optimisation results from individual case studies to many other energy systems is summarised. In the first step, a typology of the energy systems to be investigated was created. Based on this typology, representative energy systems were selected and analysed in an energy system optimisation model. In the third step, the results of the representative case studies were transferred to all other energy systems. This approach was applied to a case study for determining the minimum costs of energy system transformation for all 11,131 German municipalities from 2015 to 2035 in the completely energy autonomous case. While a technical potential to achieve energy autonomy is present in 56% of the German municipalities, energy autonomy shows only low economic potential under current energy-political conditions. However, energy system costs in the autonomous case can be greatly reduced by the installation and operation of base-load technologies like deep-geothermal plants combined with district heating networks. The developed approach can be applied to any type of energy system and should help decision makers, policy makers and researchers to estimate optimal results for a variety of energy systems using practical computational expenses.

Recoupling Climate Change and Air Quality: Exploring Low-Emission Options in Urban Transportation Using the TIMES-City Model

Fossil fuels in transportation are a significant source of local emissions in and around cities; thus, decarbonising transportation can reduce both greenhouse gases (GHGs) and air pollutants (APs). However, the degree of these reductions depends on what replaces fossil fuels. Today, GHG and AP mitigation strategies are typically ‘decoupled’ as they have different motivations and responsibilities. This study investigates the ancillary benefits on (a) APs if the transport sector is decarbonised, and (b) GHGs if APs are drastically cut and (c) the possible co-benefits from targeting APs and GHGs in parallel, using an energy-system optimisation model with a detailed and consistent representation of technology and fuel choices. While biofuels are the most cost-efficient option for meeting ambitious climate-change-mitigation targets, they have a very limited effect on reducing APs. Single-handed deep cuts in APs require a shift to zero-emission battery electric and hydrogen fuel cell vehicles (BEVs, HFCVs), which can result in significant upstream GHG emissions from electricity and hydrogen production. BEVs powered by ‘green’ electricity are identified as the most cost-efficient option for substantially cutting both GHGs and APs. A firm understanding of these empirical relationships is needed to support comprehensive mitigation strategies that tackle the range of sustainability challenges facing cities.

Incorporating consumer choice into an optimization model for the German heat sector: Effects on projected bioenergy use

The energy transition requires policy makers to adopt a holistic view that also considers non-economic factors when developing cleaner technology deployment schemes. In particular, a broad knowledge base is required to ensure an efficient energetic use of the limited biomass potential. Energy system optimization models are widely used to inform decision makers about energy transition strategies. The heterogeneity of consumers, especially in the heat sector, is rarely considered in these models and therefore these models lack of completion to contribute to this holistic approach. In this study, a literature review was conducted to find empirical data on consumer behavior regarding the adoption of residential heating systems. This data was integrated into an optimization model for the German heat sector, combining established methods for integrating consumer heterogeneity with a novel approach for calculating indirect costs representing behavioral factors. The incorporation of consumer choice leads to a broader distribution of market shares of different technologies in both a “business-as-usual” scenario and an “ambitious measures” scenario. In particular, the future role of log wood technologies in the private household sector may have been underestimated in previous studies and should be discussed, when designing policies. With this study, the knowledge base for decision makers was extended to discuss the future efficient use of biomass within the German heat sector.

Capacity credit of storage in long-term planning models and capacity markets

Capacity credits, i.e., metrics that describe the contribution of different technologies in meeting the load during peak periods, are widely used in the context of long-term energy-system optimization models to ensure a pre-defined level of firm capacity. In the same vain, such capacity credits may be used in capacity markets to reflect the availability of an asset during periods of peak load. For storage technologies it seems that there is a discrepancy between the capacity credit that correctly captures the capacity contribution to the capacity target, and the capacity credit that correctly values the storage capacity. This is illustrated in a case study, which shows the differences in planning model outcomes when different capacity credit interpretations are used. Our results indicate that defining the capacity credit according to the contribution to the capacity target overvalues storage technologies, causing overinvestments. On the contrary, defining the capacity credit to reflect the value of the storage capacity, triggers the correct amount of storage investments, but underestimates the true peak reduction potential, which results in overinvestments in other firm capacity providers. In this regard, a novel iterative approach is introduced that leverages both capacity credit interpretations simultaneously to remove any bias in the solution.

To Prevent or Promote Grid Expansion? Analyzing the Future Role of Power Transmission in the European Energy System

Future energy supply systems must become more flexible than they are today to accommodate the significant contributions expected from intermittent renewable power sources. Although numerous studies on planning flexibility options have emerged over the last few years, the uncertainties related to model-based studies have left the literature lacking a proper understanding of the investment strategy needed to ensure robust power grid expansion. To address this issue, we focus herein on two important aspects of these uncertainties: the first is the relevance of various social preferences for the use of certain technologies, and the second is how the available approaches affect the flexibility options for power transmission in energy system models. To address these uncertainties, we analyze a host of scenarios. We use an energy system optimization model to plan the transition of Europe’s energy system. In addition to interacting with the heating and transport sectors, the model integrates power flows in three different ways: as a transport model, as a direct current power flow model, and as a linearized alternating current power flow model based on profiles of power transfer distribution factors. The results show that deploying transmission systems contribute significantly to system adequacy. If investments in new power transmission infrastructure are restricted—for example, because of social opposition—additional power generation and storage technologies are an alternative option to reach the necessary level of adequacy at 2% greater system costs. The share of power transmission in total system costs remains widely stable around 1.5%, even if cost assumptions or the approaches for modeling power flows are varied. Thus, the results indicate the importance of promoting investments in infrastructure projects that support pan-European power transmission. However, a wide range of possibilities exists to put this strategy into practice.

Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model

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.

OPERA: a New High-Resolution Energy System Model for Sector Integration Research

This article introduces and describes OPERA, a new technology-rich bottom-up energy system optimization model for the Netherlands. We give a detailed specification of OPERA’s underlying methodology and approach, as well as a description of its multiple applications. The model has been used extensively to formulate strategic policy advice on energy decarbonization and climate change mitigation for the Dutch government, and to perform exploratory studies on the role of specific low-carbon energy technologies in the energy transition of the Netherlands. Based on a reference scenario established through extensive consultation with industry and the public sector, OPERA allows for examining the impact of autonomous technology diffusion and energy efficiency improvement processes, and investigating a broad range of policy interventions that target greenhouse gas emission abatement or air pollution reduction, amongst others. Two characteristics that render OPERA particularly useful are the fact that (1) it covers the entire energy system and all greenhouse gas emissions of the Netherlands, and (2) it possesses a high temporal resolution, including a module for flexibly handling individual time units. The simulation of the complete Dutch energy system and the ability to represent energy supply and demand on an hourly basis allow for making balanced judgements on how to best accommodate large amounts of variable renewable energy production. OPERA is therefore particularly suited for analyzing subjects in the field of system integration, which makes it an ideal tool for assessing the implementation of the energy transition and the establishment of a low-carbon economy. We outline several near-term developments as well as opportunities for future improvements and refinements of OPERA. One of OPERA’s attractive features is that its structure can readily be applied to other countries, notably in Europe, and could thus relatively easily be extended to cover the entire European Union

Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico

Energy system optimization models often incorporate climate change impacts to examine different energy futures and draw insights that inform policy. However, increased risk of extreme weather events from climate change has proven more difficult to model. Here, we present an energy system optimization model that incorporates hurricane risks by combining storm probabilities with infrastructure fragility curves, and demonstrate its utility in the context of Puerto Rico, an island territory of the United States that had its energy system severely damaged by Hurricane Maria in 2017. The model assesses the potential to change grid architecture, fuel mix and grid hardening measures considering hurricane impacts as well as climate mitigation policies. When hurricane trends are included, 2040 electricity cost projections increase by 32% based on historical hurricane frequencies and by 82% for increased hurricane frequencies. Transitioning to renewables and natural gas reduces costs and emissions independent of climate mitigation policies. In order to assess the impact of climate change on energy systems, models need to incorporate the increased risk of extreme weather events. Here, Bennett et al. provide a framework to integrate increasing extreme event risk in grid expansion planning models and apply the method to hurricane risks in Puerto Rico.