Energy System Optimization Model Research Articles

Representing decentralized generation and local energy use flexibility in an energy system optimization model

Local energy generation and energy use flexibility is becoming increasingly relevant for planning energy systems with a high level of renewables, electrification, and decentralization. Although current energy system models represent various flexibility options, capturing demand-side flexibility and prosumption remains challenging. In light of these challenges, this study presents a national-scale energy system optimization model covering the residential and commercial sectors of Belgium and considering the interplay between local energy use flexibility from electric vehicle (EV) charging, battery storage, rooftop photovoltaic (PV) systems and distribution grids. To adequately represent energy use flexibility, the model adopts a high temporal resolution, consisting of three representative weeks at an hourly time scale. The role of prosumption is analysed by introducing a prosumer potential reflecting the extent to which local energy production can be used to meet local demand ‘behind the meter’. The results show that flexibility significantly reduces total system costs with reduced distribution grid capacity investment by 48% by 2050. Furthermore, local energy use flexibility enables a significant uptake of rooftop PV (+6.7 GW), but only under a high prosumer potential condition. Finally, a curtailment of around 5–7% of PV production is cost-effective to sustain a more continuous use of the distribution grid at low peak capacity. To increase the robustness of the results, future research should consider expanding the model's sector coverage, adding additional flexibility sources, improving the representation of prosumption, soft-linking with distribution grid models and a better representation of mobility patterns.

A comprehensive assessment of cost-optimal heat supply to new low-energy building areas in Sweden

Buildings in Sweden use a large share of energy produced nationally but produce a low share of direct carbon emissions due to high market share of heat pumps and district heating (DH), which uses biomass. Sweden has set stringent energy and climate goals for 2045 prescribing, among other things, reduced energy consumption in buildings. Consequently, new building areas are built based on low-energy building (LEB) standards. These buildings require space heating on cold days and hot water generally. There are four main heat supply options for these areas: (1) individual heat devices (2) decentralized DH, (3) connection to a nearby centralized DH, and (4) prosumers. In this study we developed and applied a dynamic energy system optimization model and designed a systematic analysis to assess cost-optimal heat supply to LEBs in the long-term, while addressing the climate targets. The results showed that low temperature decentralized DHs (LTDDH)s have the lowest cost and individual heat devices have the highest cost of heat supply to LEB areas. The cost-efficiency of LTDDH increases with if prosumers exist in more densely built LEB areas with higher annual heat demand. Additionally, stringent climate policy scenarios can further decrease the cost of LTDDH in LEB areas.

Model building of urban energy system and effect analysis of green low‐carbon transition under the background of carbon peaking and carbon neutrality

AbstractUnder the carbon peaking and carbon neutrality goals background in China, cities are the main force of energy consumption and carbon emission, and their low‐carbon transition development has become the primary task of urban planning. The transitional development of cities not only puts forward higher requirements for the output proportion of new energy, but also puts forward higher requirements for the flexibility of energy system. This paper proposes an optimization model of urban energy system considering the uncertainty output of new energy and new energy vehicles. The uncertainty and operational flexibility of electric vehicles are taken into account at the planning level by using stochastic optimization method. The model considers the expansion and retirement of new energy units and various conversion devices in urban energy system, and ensures the achievement of carbon peaking and carbon reduction goals by imposing constraints on electric vehicles. The case results show that this model can effectively improve the economy and new energy consumption rate of urban energy system, and can meet the carbon reduction demand.

3E (energy, economic, environmental) multi-objective optimization of CCHP industrial plant: Investigation of the optimal technology and the optimal operating strategy

The article describes the application of an innovative multi-objective optimization model for tri-generative energy systems, based on three objective functions: technical, economic, and environmental. The proposed algorithm was used to simulate the daily operation of several tri-generative system configurations, counting for a total of 31,680 simulations. Thereafter, a critical examination of the data was carried out and then comparisons were made between the 22 studied technology sets, by considering internal combustion engines, solid oxide fuel cells, proton exchange membrane fuel cells, absorption heat pumps, and compression gas heat pumps, lithium-ion, and lead-acid batteries. For the analyzed case study, relating to a large industrial user, the average electrical load in the four reference periods is equal to 4970 kW. The optimal tri-generation plant for this type of user turned out to be composed of: SOFC fueled by methane with a size of 5 MWe, a lithium-ion storage system with a maximum size of 500 kW, an absorption heat pump with a size of 390 kW, gas compression heat pump for the generation of cooling energy with size equal to 390 kW, and gas compression heat pump for the generation of thermal energy with size equal to 1600 kW.

Research status and prospect of key problems of integrated energy system optimization model

In the traditional energy system, the operation structure of various energy sources is single, the coupling degree is not high, the efficiency of energy utilization is low, and the waste of clean energy appears, so the concept of comprehensive energy system arises at the historic moment.This paper firstly introduces the composition of the integrated energy system, summarizes the research status of the network power flow model of electric power, thermal power and natural gas, and summarizes the optimization operation method, leading to the integrated energy system source-source optimization model.The objective function, constraint condition, solution algorithm and operation cost of the optimization system are analyzed, and the key scientific problems and future research direction of the optimization management are put forward

Study on the Optimal Configuration of GSHP in Regional Integrated Energy Systems Considering the Uncertainty of New Energy

The study of regionally integrated energy system optimization problems is of great significance and usefulness for the development of the energy Internet. Regional integrated energy system planning and optimization modeling is the basis for rational system configuration and economical operation. It is one of the key issues in the development of integrated regional energy systems. The optimization of a regionally integrated energy system is studied in this paper. The uncertainty of wind power generation and photovoltaic power generation and the characteristic of CHP is considered. Were established. On this basis, Considering the uncertainty of new energy, a GSHP optimal allocation model based on two-layer optimization is established. A two-level optimization model is introduced. A genetic algorithm is applied to the upper layer, and a genetic algorithm is applied to the lower layer to analyze the nonlinear programming problem. Finally, different system configuration schemes are proposed and computationally solved, combined with examples for case studies to verify the feasibility of the established regional integrated energy system optimization model.

PyPSA-Earth. A new global open energy system optimization model demonstrated in Africa

Macro-energy system modelling is used by decision-makers to steer the global energy transition towards an affordable, sustainable and reliable future. Closed-source models are the current standard for most policy and industry decisions. However, open models have proven to be competitive alternatives that promote science, robust technical analysis, collaboration and transparent policy decision-making. Yet, two issues slow the adoption: open models are often designed with particular geographic scope in mind, thus hindering synergies from collaborating, or are based on low spatially resolved data, limiting their use. Here we introduce PyPSA-Earth, an open-source global energy system model with data in high spatial and temporal resolution. It enables large-scale collaboration by providing a tool that can model the world’s energy system or any subset of it. The model is suitable for operational as well as combined generation, storage and transmission expansion studies. In this study, the novel power system capabilities of PyPSA-Earth are highlighted and demonstrated. The model provides two main features: (1) customizable data extraction and preparation with global coverage and (2) a PyPSA energy modelling framework integration. The data includes electricity demand, generation and medium to high-voltage networks from open sources, yet additional data can be further integrated. A broad range of clustering and grid meshing strategies help adapt the model to computational and practical needs. Data validation for the entire African continent is performed and the optimization features are tested with a 2060 net-zero planning study for Nigeria. The demonstration shows that the presented developments can build a highly detailed power system model for energy planning studies to support policy and technical decision-making. We anticipate that PyPSA-Earth can represent an open reference model for system planning, and we welcome joining forces to address the challenges of the energy transition together.

AEM-electrolyzer based hydrogen integrated renewable energy system optimisation model for distributed communities

The development of sustainable and renewable energy technologies has received significant attention to realize Net-Zero CO2 equivalent emission goals and meet the growing energy demand. Hydrogen is a promising energy carrier that can facilitate the large-scale deployment of renewable energy sources and assist in the replacement of fossil fuels and to reduce the impact of global warming. The objective of this research is to present an advanced hydrogen-integrated renewable energy system model to meet the energy demand of a distributed community and produce green hydrogen from excess/curtailed renewable energy. The study employs an anion exchange membrane water electrolyzer (AEM) for producing hydrogen. An optimization model of the renewable energy system and a mathematical model of the electrolyzer are developed to achieve this objective. The model uses an energy maximisation approach and optimally combines wind system, biogas plant, and solar PV system to meet the residential and commercial load demands. To increase the system stability, the model is interconnected with the local grid station for energy exchange. Moreover, an uncertainty analysis is also performed to analyse the system response under random variation in load demand. The study results show that a significant amount of clean energy (15,025 MWh/year) is produced by the system at the lowest levelized cost of 0.084 €/kWh and a reduction of 6,078 tons of CO2 emission during the first year of operation is obtained. The electrolyzer produces 63 kg/hr of hydrogen, while the cell performance remains stable at 60 °C and the cell voltage reaches 2.019 V at 2.415 A/cm2 current density.

Green energy carriers and energy sovereignty in a climate neutral European energy system

Meeting the goals of the Paris Agreement poses significant challenges to provide renewable energy for the power, heating, transport, and industrial sector. Both green hydrogen and methane are considered key energy carriers for reaching these climate targets. However, future needs for an effective infrastructure deployment are highly uncertain, particularly concerning the timely and substantial expansion of renewable electricity generation in Europe. To better understand the trade-offs between domestic production and large-scale energy imports and their corresponding infrastructures needs, we use the energy system optimisation model REMix. We consider different strategic European story lines and constraints on expansion of pipelines and power grids to identify robust capacity targets from a cost optimal perspective. The results indicate that European energy sovereignty is feasible but comes at around 3% higher cost compared to stronger cooperation with resource-rich areas such as the British Isles or the Maghreb region. In contrast, preventing any network expansion lead to an increase of up to 15%. With regard to the extensive adaptations of energy infrastructures required to achieve the emission reduction goal, the timely and substantial expansion of electricity generation from renewable sources in particular is to be regarded as crucial.

Communal or individual – Exploring cost-efficient heating of new city-level housing in a systems perspective

As cities expand, new buildings are constructed and they require heating. With increasing integration of the heating and electricity sectors and forecasts of rapid growth in electricity demand, heating choices become critical for the sustainability transition. The main heating options are communal or individual, where the communal option is represented by district heating (DH) and the individual option mainly by heat pumps or biomass heating. Which option is best from the cost perspective depends on the building type and on the energy system development. Thus, this paper investigates cost-efficient heating of new city-level housing in a systems perspective under various scenarios.The investigation was carried out using an energy systems optimization model based on a case representing Swedish conditions. A dynamic approach was used to investigate cost-efficient development of the supply side and demand side simultaneously.The results indicate that the most cost-efficient heating systems are: DH for apartment buildings; and individual heating options for single-family housing with low heat demands. For large single-family housing with high heat demands, the cost-efficient solution depends on the heat demand profile. Higher heat use during winter favors DH and individual biomass boilers, but diminishes the economic feasibility of individual heat pumps.

Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models

Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., a priori evaluation). However, its impact on the results of the energy system model (i.e., a posteriori evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up a posteriori metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this a priori metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.

Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling.

Background: Energy system optimisation models (ESOMs) are commonly used to support long-term planning at national, regional, or continental scales. The importance of recognising uncertainty in energy system modelling is regularly commented on but there is little practical guidance on how to best incorporate existing techniques, such as global sensitivity analysis, despite some good applications in the literature. Methods: In this paper, we provide comprehensive guidelines for conducting a global sensitivity analysis of an ESOM, aiming to remove barriers to adopting this approach. With a pedagogical intent, we begin by exploring why you should conduct a global sensitivity analysis. We then describe how to implement a global sensitivity analysis using the Morris method in an ESOM using a sequence of simple illustrative models built using the Open Source energy Modelling System (OSeMOSYS) framework, followed by a realistic example. Results: Results show that the global sensitivity analysis identifies influential parameters that drive results in the simple and realistic models, and identifies uninfluential parameters which can be ignored or fixed. We show that global sensitivity analysis can be applied to ESOMs with relative ease using freely available open-source tools. The results replicate the findings of best-practice studies from the field demonstrating the importance of including all parameters in the analysis and avoiding a narrow focus on particular parameters such as technology costs. Conclusions: The results highlight the benefits of performing a global sensitivity analysis for the design of energy system optimisation scenarios. We discuss how the results can be interpreted and used to enhance the transparency and rigour of energy system modelling studies.

Optimal design of microgrids to improve wildfire resilience for vulnerable communities at the wildland-urban interface

Climate change leads to extreme climate events that result in frequent wildfires that cause numerous adverse societal impacts. Public Safety Power Shutoffs, adopted by utilities to minimize the risk of wildfires, pose many challenges to electricity consumers. Microgrids, have been proposed to improve the resilience of energy infrastructure during wildfire events for vulnerable communities. However, a comprehensive techno-economic and environmental assessment of the potential of such energy systems have not been performed. To address this research gap, the present study introduces a modeling framework, consisting of (1) clustering algorithms that identify the communities based on building footprint data, fire hazard severity, and renewable energy potential; (2) a building simulation model to quantify the energy demand; and (3) an energy system optimization model to assist the Microgrid design. A novel optimization tool was introduced to model Microgrids in wildland-urban interface, and subsequently, a comprehensive assessment was performed, focusing on seven localities from California, United States, with different climatic conditions. The study reveals that Microgrids can keep the average levelized energy cost and annual Public Safety Power Shutoffs below $0.3/kilowatt-hour (kWh) and 2%–3% (of the annual energy demand), respectively. Furthermore, renewable energy penetration levels can be maintained above 60% of the annual energy demand. Therefore, Microgrid may become an attractive solution to reduce the adverse impacts of wildfires and enhance the resilience of energy infrastructure. However, the study reveals that Microgrid cannot completely eliminate the Public Safety Power Shutoffs. The levelized cost and renewable energy generation curtailments (waste of renewable energy) become notably high when attempting to eliminate Public Safety Power Shutoffs completely. A notable reduction in energy storage cost is essential to achieve zero Public Safety Power Shutoffs, and this is expected with the evolution of energy storage technologies. The present study recommends Microgrids for communities affected by wildfires to enhance the resilience of energy infrastructure and protect the health and safety of residents. The modeling framework and optimization tool developed in this study can be used by stakeholders and their consultants to inform design and optimization of Microgrids for investment decision making.

Interactions between land and grid development in the transition to a decarbonized European power system

The decarbonization of the European electricity system by 2050 will involve replacing fossil fuels with low-carbon energies, in particular variable renewable energies, coupled with the development of the transmission network to ensure their integration at European scale. As these production modes have a lower energy density, their development will imply an increase in the amount of land dedicated to electricity production. This study investigates the links between an optimal decarbonization of the electricity system, land requirements, and the development strategy of interconnections. We study these interactions using eTIMES-EU, an energy system optimization model, by endogenously taking into account a land variable and testing two interconnection development plans. Our results show that it is possible to limit the land use of the network to its 2020 value for roughly a one percent higher total discounted cost, and that the development of interconnections changes the technologies to be installed on a European scale, but that it requires efficient and ambitious planning. Moreover, our results highlight disparities across countries in terms of space and investment that may constitute barriers to implementing this type of centralized development policy.

Resilient energy system analysis and planning using optimization models

Imminent climate change impacts call for stronger energy system modeling approaches in order to design resilient communities. This study presents a flexible framework to integrate resilience analysis within the scope of long-term energy system optimization models (ESOMs). It employs a multi-objective resilience metric approach for energy system design, which allows for the independent representation and treatment of resilience and sustainability metrics. Several energy system-characterizing resilience and sustainability metrics are identified and integrated into a composite resilience metric, which is maximized as the objective function in an open-source ESOM. The cost performance of the resulting energy system design is tested across a range of short-term resilience scenarios, capturing different shocks. The method is demonstrated on two municipal case studies (located in China and Ghana). Three energy systems are designed and compared based on cost, emission, and resilience optimization objectives. Results illustrate a wide range of cost impacts depending on the system and resilience scenario. Systems designed based on a resilience objective offer more flexibility to adapt to and absorb shocks, thus reducing damage costs. Case study findings illustrate the value of incorporating resilience analysis into conventional ESOM and energy planning approaches in order to build more resilient communities.

Exploring the role of electrification and modal shift in decarbonizing the road passenger transport in British Columbia

The possibility of the modal shift to public transport and active mobility while considering transport electrification and fuel efficiency improvement has yet to be adequately investigated. This paper explores transition pathways toward an environmentally sustainable road passenger transportation system in the province of British Columbia (BC), Canada. MESSAGE, as a bottom-up energy systems optimization model, is used to find the cost-optimal fuel and technology mix in the transport and power sector. Multiple scenarios mainly assess the influence of modal shift and electric vehicle (EV) diffusion on greenhouse gas emissions by 2050. Besides, the effects of scenarios on the power sector configuration are examined. The results show that BC would not achieve the 80% emissions reduction target in the Climate Change Accountability Act unless by a radical expansion of transport electrification. The target could be met by a minimum diffusion of 70% EVs in the total car stock as well as 35% public transport contribution in total passenger kilometers. The findings also indicate that fully electrified light-duty vehicles coupled with active transport would lead to almost a zero-emission level. Nevertheless, 100% electrification would impose an extra 5.6 TWh burden on the power supply system relative to the business-as-usual scenario.

Improving energy system design with optimization models by quantifying the economic granularity gap: The case of prosumer self-consumption in Germany

Energy system models are widely used to inform the political decisions required to successfully mitigate climate change in the energy sector. The energy system optimization models (ESOMs) used to identify cost-minimal transformation pathways assume the perfect behavior of market participants from a central planner’s perspective. Neglecting the decision-making under uncertainties or biased perceptions and attitudes leads to inaccurate assumptions regarding the requirements of a successful energy transition. In particular, ESOMs underestimate the required capacities for power generation, storage, and transmission compared with real-world energy systems, a phenomenon known as the “economic granularity gap”. Agent-based models (ABMs) are helpful tools for capturing the behavior of market actors. Hence, attempts have been made to identify and alleviate this phenomenon through the coupling of ESOMs and ABMs. In this paper, we propose an automated workflow for such model coupling and quantify the economic granularity gap for the case of photovoltaic-prosumer self-consumption. Our results show that the current business models and regulatory frameworks affecting prosumer self-consumption patterns require the adaptation of cost-minimal energy system designs. However, if correctly implemented, instruments such as dynamic tariffs could narrow the economic granularity gap, shifting real-world energy systems closer to their ideal counterparts.

Intersecting near-optimal spaces: European power systems with more resilience to weather variability

We suggest a new methodology for designing robust energy systems. For this, we investigate so-called near-optimal solutions to energy system optimisation models; solutions whose objective values deviate only marginally from the optimum. Using a refined method for obtaining explicit geometric descriptions of these near-optimal feasible spaces, we find designs that are as robust as possible to perturbations. This contributes to the ongoing debate on how to define and work with robustness in energy systems modelling.We apply our methods in an investigation using multiple decades of weather data. For the first time, we run a capacity expansion model of the European power system (one node per country) with a three-hourly temporal resolution and 41 years of weather data. While an optimisation with 41 weather years is at the limits of computational feasibility, we use the near-optimal feasible spaces of single years to gain an understanding of the design space over the full time period. Specifically, we intersect all near-optimal feasible spaces for the individual years in order to get designs that are likely to be feasible over the entire time period. We find significant potential for investment flexibility, and verify the feasibility of these designs by simulating the resulting dispatch problem with four decades of weather data. They are characterised by a shift towards more onshore wind and solar power, while emitting more than 50% less CO2 than a cost-optimal solution over that period.Our work builds on recent developments in the field, including techniques such as Modelling to Generate Alternatives (MGA) and Modelling All Alternatives (MAA), and provides new insights into the geometry of near-optimal feasible spaces and the importance of multi-decade weather variability for energy systems design. We also provide an effective way of working with a multi-decade time frame in a highly parallelised manner. Our implementation is open-sourced, adaptable and is based on PyPSA-Eur.

A Model-Adaptive Clustering-Based Time Aggregation Method for Low-Carbon Energy System Optimization

Intermittent renewable energy resources like wind and solar introduce uncertainty across multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution that manages the multi-timescale variability using an optimal portfolio of flexible resources. However, input data that capture such multi-timescale uncertainty are characterized with a long time horizon and high resolution, which brings great difficulty to solving the optimization model. Here we propose a model-adaptive time aggregation method based on clustering to alleviate the computational complexity, in which the energy system is solved over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models to inform the clustering process. Results show that the proposed adaptive method can significantly lower the error in approximating the solution of the optimization model with the full time horizon, compared to traditional time aggregation methods.

Analysing municipal energy system transformations in line with national greenhouse gas reduction strategies

Climate change mitigation and transformation strategies with expansion targets for renewable energy sources are defined at the national level. Due to the decentralised character of these sources, local energy system planning plays an important role. However, local communities often lack the capacity to develop energy concepts and thus exploit local renewable potentials consistently. This study develops a highly transferable methodology for deriving local energy system transformation scenarios in line with national greenhouse gas reduction strategies. Thus, an energy system optimisation model is substantially extended to collectively optimise the transformation of final energy demand in the residential, industry, tertiary and transport sectors, as well as established and niche greenhouse gas reduction technologies. Here, a focus is set on the building stock transformation, and a stochastic model is presented to better grasp and represent the dynamic developments and heterogeneity of the local building stock. Based on superordinate parameters such as retrofit rates and heating technology diffusion, the stochastic model generates informative building stock scenarios that are used as input for the developed energy system optimisation model. Exemplarily, the model is applied to the central European city of Karlsruhe. The results show that an increase of the retrofit rate to 2 %/a and strong electrification of the heat supply in the building sector is economically and environmentally beneficial. Furthermore, an accelerated expansion of photovoltaics compared to the national expansion rate can save costs and CO2 emissions. Building on the methodology presented, transferable infrastructure models for the electricity, gas and district heating network should be developed that can be used to assess the feasibility of the transformation paths determined by the methodology presented.