Electricity Modelling

[Odon Irving]

Energymodeling 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 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]

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]

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.

In addition to the electricity sector, energy system models include the heat, gas, mobility, and other sectors as appropriate.[26] 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.[27]

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.[29]

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.[30][31]

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.[32][33][34][35][36][37][38][39]

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.

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.[44] NEMS has been used to explicitly model the demand-side, in particular to determine consumer technology choices in the residential and commercial building sectors.[45]

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.[47] 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.[48][49] 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.[50][51][52][53]

The Agency assists Member States with practical solutions for their energy planning. It offers different types of energy modelling tools that enable States to make smart energy choices. The IAEA's Planning and Economic Studies Section develops, enhances, maintains and transfers analytical tools to assess different energy options and strategies, including the potential of nuclear power.

The Section offers to Member States a wide-range of tools for integrated energy planning for sustainable development that are delivered through computer based software programs and manuals, trainings and e-learning sessions/platforms, upon request.

MAED evaluates future energy demands based on medium- to long-term scenarios of socioeconomic, technological and demographic development. Energy demand is disaggregated into a large number of end-use categories corresponding to different goods and services. The influences of social, economic and technological driving factors from a given scenario are estimated. These are combined for an overall picture of future energy demand growth. The computer manual for the Model for Analysis of Energy Demand (MAED-2) is available in English, French and Spanish.

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