Energy modeling or energy system modeling is the process of building computer models of energy systems in order to analyze them. Such models often employ scenario analysis to investigate different assumptions about the technical and economic conditions at play. Outputs may include the system feasibility, greenhouse gas emissions, cumulative financial costs, natural resource use, and energy efficiency of the system under investigation. A wide range of techniques are employed, ranging from broadly economic to broadly engineering. [1] Mathematical optimization is often used to determine the least-cost in some sense. Models can be international, regional, national, municipal, or stand-alone in scope. Governments maintain national energy models for energy policy development.
Energy models are usually intended to contribute variously to system operations, engineering design, or energy policy development. This page concentrates on policy models. Individual building energy simulations are explicitly excluded, although they too are sometimes called energy models. IPCC-style integrated assessment models, which also contain a representation of the world energy system and are used to examine global transformation pathways through to 2050 or 2100 are not considered here in detail.
Energy modeling has increased in importance as the need for climate change mitigation has grown in importance. The energy supply sector is the largest contributor to global greenhouse gas emissions. [2] The IPCC reports that climate change mitigation will require a fundamental transformation of the energy supply system, including the substitution of unabated (not captured by CCS) fossil fuel conversion technologies by low-GHG alternatives. [2]
A wide variety of model types are in use. This section attempts to categorize the key types and their usage. The divisions provided are not hard and fast and mixed-paradigm models exist. In addition, the results from more general models can be used to inform the specification of more detailed models, and vice versa, thereby creating a hierarchy of models. Models may, in general, need to capture "complex dynamics such as:
Models may be limited in scope to the electricity sector or they may attempt to cover an energy system in its entirety (see below).
Most energy models are used for scenario analysis. A scenario is a coherent set of assumptions about a possible system. New scenarios are tested against a baseline scenario – normally business-as-usual (BAU) – and the differences in outcome noted.
The time horizon of the model is an important consideration. Single-year models – set in either the present or the future (say 2050) – assume a non-evolving capital structure and focus instead on the operational dynamics of the system. Single-year models normally embed considerable temporal (typically hourly resolution) and technical detail (such as individual generation plant and transmissions lines). Long-range models – cast over one or more decades (from the present until say 2050) – attempt to encapsulate the structural evolution of the system and are used to investigate capacity expansion and energy system transition issues.
Models often use mathematical optimization to solve for redundancy in the specification of the system. Some of the techniques used derive from operations research. Most rely on linear programming (including mixed-integer programming), although some use nonlinear programming. Solvers may use classical or genetic optimisation, such as CMA-ES. Models may be recursive-dynamic, solving sequentially for each time interval, and thus evolving through time. Or they may be framed as a single forward-looking intertemporal problem, and thereby assume perfect foresight. Single-year engineering-based models usually attempt to minimize the short-run financial cost, while single-year market-based models use optimization to determine market clearing. Long-range models, usually spanning decades, attempt to minimize both the short and long-run costs as a single intertemporal problem.
The demand-side (or end-user domain) has historically received relatively scant attention, often modeled by just a simple demand curve. End-user energy demand curves, in the short-run at least, are normally found to be highly inelastic.
As intermittent energy sources and energy demand management grow in importance, models have needed to adopt an hourly temporal resolution in order to better capture their real-time dynamics. [4] [5] Long-range models are often limited to calculations at yearly intervals, based on typical day profiles, and are hence less suited to systems with significant variable renewable energy. Day-ahead dispatching optimization is used to aid in the planning of systems with a significant portion of intermittent energy production in which uncertainty around future energy predictions is accounted for using stochastic optimization. [6]
Implementing languages include GAMS, MathProg, MATLAB, Mathematica, Python, Pyomo, R, Fortran, Java, C, C++, and Vensim. Occasionally spreadsheets are used.
As noted, IPCC-style integrated models (also known as integrated assessment models or IAM) are not considered here in any detail. [7] [8] Integrated models combine simplified sub-models of the world economy, agriculture and land-use, and the global climate system in addition to the world energy system. Examples include GCAM, [9] MESSAGE, and REMIND. [10]
Published surveys on energy system modeling have focused on techniques, [11] general classification, [12] an overview, [13] decentralized planning, [14] modeling methods, [15] renewables integration, [6] [16] energy efficiency policies, [17] [18] electric vehicle integration, [19] international development, [20] and the use of layered models to support climate protection policy. [21] Deep Decarbonization Pathways Project researchers have also analyzed model typologies. [3] : S30–S31 A 2014 paper outlines the modeling challenges ahead as energy systems become more complex and human and social factors become increasingly relevant. [22]
Electricity sector models are used to model electricity systems. The scope may be national or regional, depending on circumstances. For instance, given the presence of national interconnectors, the western European electricity system may be modeled in its entirety.
Engineering-based models usually contain a good characterization of the technologies involved, including the high-voltage AC transmission grid where appropriate. Some models (for instance, models for Germany) may assume a single common bus or "copper plate" where the grid is strong. The demand-side in electricity sector models is typically represented by a fixed load profile.
Market-based models, in addition, represent the prevailing electricity market, which may include nodal pricing.
Game theory and agent-based models are used to capture and study strategic behavior within electricity markets [23] [24] [25] and analyze the integration of renewable energies as part of the energy transition. [26]
In addition to the electricity sector, energy system models include the heat, gas, mobility, and other sectors as appropriate. [27] Energy system models are often national in scope, but may be municipal or international.
So-called top-down models are broadly economic in nature and based on either partial equilibrium or general equilibrium. General equilibrium models represent a specialized activity and require dedicated algorithms. Partial equilibrium models are more common.
So-called bottom-up models capture the engineering well and often rely on techniques from operations research. Individual plants are characterized by their efficiency curves (also known as input/output relations), nameplate capacities, investment costs (capex), and operating costs (opex). Some models allow for these parameters to depend on external conditions, such as ambient temperature. [28]
Producing hybrid top-down/bottom-up models to capture both the economics and the engineering has proved challenging. [29]
This section lists some of the major models in use. [1] These are typically run by national governments. In a community effort, a large number of existing energy system models were collected in model fact sheets on the Open Energy Platform. [30]
LEAP, the Low Emissions Analysis Platform (formerly known as the Long-range Energy Alternatives Planning System) is a software tool for energy policy analysis, air pollution abatement planning and climate change mitigation assessment. [31] [32]
LEAP was developed at the Stockholm Environment Institute's (SEI) US Center. LEAP can be used to examine city, statewide, national, and regional energy systems. LEAP is normally used for studies of between 20–50 years. Most of its calculations occur at yearly intervals. LEAP allows policy analysts to create and evaluate alternative scenarios and to compare their energy requirements, social costs and benefits, and environmental impacts. As of June 2021, LEAP has over 6000 users in 200 countries and territories
General Electric's MAPS (Multi-Area Production Simulation) is a production simulation model used by various Regional Transmission Organizations and Independent System Operators in the United States to plan for the economic impact of proposed electric transmission and generation facilities in FERC-regulated electric wholesale markets. Portions of the model may also be used for the commitment and dispatch phase (updated on 5 minute intervals) in operation of wholesale electric markets for RTO and ISO regions. ABB's PROMOD is a similar software package. These ISO and RTO regions also utilize a GE software package called MARS (Multi-Area Reliability Simulation) to ensure the power system meets reliability criteria (a loss of load expectation (LOLE) of no greater than 0.1 days per year). Further, a GE software package called PSLF (Positive Sequence Load Flow) and a Siemens software package called PSSE (Power System Simulation for Engineering) analyzes load flow on the power system for short-circuits and stability during preliminary planning studies by RTOs and ISOs. [33] [34] [35] [36] [37] [38] [39] [40]
MARKAL (MARKet ALlocation) is an integrated energy systems modeling platform, used to analyze energy, economic, and environmental issues at the global, national, and municipal level over time-frames of up to several decades. MARKAL can be used to quantify the impacts of policy options on technology development and natural resource depletion. The software was developed by the Energy Technology Systems Analysis Programme (ETSAP) of the International Energy Agency (IEA) over a period of almost two decades.
TIMES (The Integrated MARKAL-EFOM System) is an evolution of MARKAL – both energy models have many similarities. [41] TIMES succeeded MARKAL in 2008. [42] Both models are technology explicit, dynamic partial equilibrium models of energy markets. In both cases, the equilibrium is determined by maximizing the total consumer and producer surplus via linear programming. Both MARKAL and TIMES are written in GAMS.
The TIMES model generator was also developed under the Energy Technology Systems Analysis Program (ETSAP). TIMES combines two different, but complementary, systematic approaches to modeling energy – a technical engineering approach and an economic approach. TIMES is a technology rich, bottom-up model generator, which uses linear programming to produce a least-cost energy system, optimized according to a number of user-specified constraints, over the medium to long-term. It is used for "the exploration of possible energy futures based on contrasted scenarios". [43] : 7
As of 2015 [update] , the MARKAL and TIMES model generators are in use in 177 institutions spread over 70 countries. [44] : 5
NEMS (National Energy Modeling System) is a long-standing United States government policy model, run by the Department of Energy (DOE). NEMS computes equilibrium fuel prices and quantities for the US energy sector. To do so, the software iteratively solves a sequence of linear programs and nonlinear equations. [45] NEMS has been used to explicitly model the demand-side, in particular to determine consumer technology choices in the residential and commercial building sectors. [46]
NEMS is used to produce the Annual Energy Outlook each year – for instance in 2015. [47]
Public policy energy models have been criticized for being insufficiently transparent. The source code and data sets should at least be available for peer review, if not explicitly published. [48] To improve transparency and public acceptance, some models are undertaken as open-source software projects, often developing a diverse community as they proceed. OSeMOSYS is an example of such a model. [49] [50] The Open Energy Outlook is an open community that has produced a long-term outlook of the U.S. energy system using the open-source TEMOA model. [51] [52] [53] [54]
Not a criticism per se, but it is necessary to understand that model results do not constitute future predictions. [55]
General
Models
Renewable energy is energy from renewable natural resources that are replenished on a human timescale. The most widely used renewable energy types are solar energy, wind power, and hydropower. Bioenergy and geothermal power are also significant in some countries. Some also consider nuclear power a renewable power source, although this is controversial. Renewable energy installations can be large or small and are suited for both urban and rural areas. Renewable energy is often deployed together with further electrification. This has several benefits: electricity can move heat and vehicles efficiently and is clean at the point of consumption. Variable renewable energy sources are those that have a fluctuating nature, such as wind power and solar power. In contrast, controllable renewable energy sources include dammed hydroelectricity, bioenergy, or geothermal power.
The International Energy Agency (IEA) is a Paris-based autonomous intergovernmental organisation, established in 1974, that provides policy recommendations, analysis and data on the global energy sector. The 31 member countries and 13 association countries of the IEA represent 75% of global energy demand.
Energy is sustainable if it "meets the needs of the present without compromising the ability of future generations to meet their own needs." Definitions of sustainable energy usually look at its effects on the environment, the economy, and society. These impacts range from greenhouse gas emissions and air pollution to energy poverty and toxic waste. Renewable energy sources such as wind, hydro, solar, and geothermal energy can cause environmental damage but are generally far more sustainable than fossil fuel sources.
Climate change mitigation (or decarbonisation) is action to limit the greenhouse gases in the atmosphere that cause climate change. Climate change mitigation actions include conserving energy and replacing fossil fuels with clean energy sources. Secondary mitigation strategies include changes to land use and removing carbon dioxide (CO2) from the atmosphere. Current climate change mitigation policies are insufficient as they would still result in global warming of about 2.7 °C by 2100, significantly above the 2015 Paris Agreement's goal of limiting global warming to below 2 °C.
Economic analysis of climate change is using economic tools and models to calculate the magnitude and distribution of damages caused by climate change. It can also give guidance for the best policies for mitigation and adaptation to climate change from an economic perspective. There are many economic models and frameworks. For example, in a cost–benefit analysis, the trade offs between climate change impacts, adaptation, and mitigation are made explicit. For this kind of analysis, integrated assessment models (IAMs) are useful. Those models link main features of society and economy with the biosphere and atmosphere into one modelling framework. The total economic impacts from climate change are difficult to estimate. In general, they increase the more the global surface temperature increases.
Mark Diesendorf is an Australian academic and environmentalist, known for his work in sustainable development and renewable energy. He currently researches at the University of New South Wales, Australia. He was formerly professor of environmental science and founding director of the Institute for Sustainable Futures at the University of Technology, Sydney and before that a principal research scientist with CSIRO, where he was involved in early research on integrating wind power into electricity grids. His most recent books are The Path to a Sustainable Civilisation (2023) and Sustainable Energy Solutions for Climate Change (2014).
Integrated assessment modelling (IAM) or integrated modelling (IM) is a term used for a type of scientific modelling that tries to link main features of society and economy with the biosphere and atmosphere into one modelling framework. The goal of integrated assessment modelling is to accommodate informed policy-making, usually in the context of climate change though also in other areas of human and social development. While the detail and extent of integrated disciplines varies strongly per model, all climatic integrated assessment modelling includes economic processes as well as processes producing greenhouse gases. Other integrated assessment models also integrate other aspects of human development such as education, health, infrastructure, and governance.
100% renewable energy is the goal of the use renewable resources for all energy. 100% renewable energy for electricity, heating, cooling and transport is motivated by climate change, pollution and other environmental issues, as well as economic and energy security concerns. Shifting the total global primary energy supply to renewable sources requires a transition of the energy system, since most of today's energy is derived from non-renewable fossil fuels.
The National Energy Modeling System (NEMS) is an economic and energy model of United States energy markets created at the U.S. Energy Information Administration (EIA). NEMS projects the production, consumption, conversion, import, export, and pricing of energy. The model relies on assumptions for economic variables, including world energy market interactions, resource availability, technological choice and characteristics, and demographics.
Abatement cost is the cost of reducing environmental negatives such as pollution. Marginal cost is an economic concept that measures the cost of an additional unit. The marginal abatement cost, in general, measures the cost of reducing one more unit of pollution. Marginal abatement costs are also called the "marginal cost" of reducing such environmental negatives.
Prospective Outlook on Long-term Energy Systems (POLES) is a world simulation model for the energy sector that runs on the Vensim software. It is a techno-economic model with endogenous projection of energy prices, a complete accounting of energy demand and supply of numerous energy vectors and associated technologies, and a carbon dioxide and other greenhouse gases emissions module.
Marilyn A. Brown is a Regents' and Brook Byers Professor of Sustainable Systems in the School of Public Policy at the Georgia Institute of Technology. She joined Georgia Tech in 2006 after 22 years at Oak Ridge National Laboratory, where she held various leadership positions. Her work was cited by President Clinton as providing the scientific justification for signing the 1997 Kyoto Protocol. With Eric Hirst, she coined the term "energy efficiency gap" and pioneered research to highlight and quantify the unexploited economic potential to use energy more productively.
The Energiewende is the ongoing energy transition by Germany to a low carbon, environmentally sound, reliable, and affordable energy supply. The new system intends to rely heavily on renewable energy, energy efficiency, and energy demand management.
Open energy system models are energy system models that are open source. However, some of them may use third party proprietary software as part of their workflows to input, process, or output data. Preferably, these models use open data, which facilitates open science.
Henrik Lund is a Danish engineer and professor at Aalborg University.
The Open Energy Modelling Initiative (openmod) is a grassroots community of energy system modellers from universities and research institutes across Europe and elsewhere. The initiative promotes the use of open-source software and open data in energy system modelling for research and policy advice. The Open Energy Modelling Initiative documents a variety of open-source energy models and addresses practical and conceptual issues regarding their development and application. The initiative runs an email list, an internet forum, and a wiki and hosts occasional academic workshops. A statement of aims is available.
An energy system is a system primarily designed to supply energy-services to end-users. The intent behind energy systems is to minimise energy losses to a negligible level, as well as to ensure the efficient use of energy. The IPCC Fifth Assessment Report defines an energy system as "all components related to the production, conversion, delivery, and use of energy".
Open energy system database projects employ open data methods to collect, clean, and republish energy-related datasets for open use. The resulting information is then available, given a suitable open license, for statistical analysis and for building numerical energy system models, including open energy system models. Permissive licenses like Creative Commons CC0 and CC BY are preferred, but some projects will house data made public under market transparency regulations and carrying unqualified copyright.
Climate change has caused temperatures in the world to rise in the last few decades, and temperatures in Europe have risen twice as fast as the average change in the rest of the world. In Spain, which already has a hot and dry climate, extreme events such as heatwaves are becoming increasingly frequent. The country is also experiencing more episodes of drought and increased severity of these episodes. Water resources will be severely affected in various climate change scenarios.
Climate change in South Africa is leading to increased temperatures and rainfall variability. Evidence shows that extreme weather events are becoming more prominent due to climate change. This is a critical concern for South Africans as climate change will affect the overall status and wellbeing of the country, for example with regards to water resources. Just like many other parts of the world, climate research showed that the real challenge in South Africa was more related to environmental issues rather than developmental ones. The most severe effect will be targeting the water supply, which has huge effects on the agriculture sector. Speedy environmental changes are resulting in clear effects on the community and environmental level in different ways and aspects, starting with air quality, to temperature and weather patterns, reaching out to food security and disease burden.