Energy modeling

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: • energy system operation • technology stock turnover • technology innovation • firm and household behavior • energy and non-energy capital investment and labor market adjustment dynamics leading to economic restructuring • infrastructure deployment and urban planning" 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 optimization, 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. 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. 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. 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, MESSAGE, and REMIND. Published surveys on energy system modeling have focused on techniques, general classification, an overview, decentralized planning, modeling methods, renewables integration, energy efficiency policies, electric vehicle integration, international development, and the use of layered models to support climate protection policy. Deep Decarbonization Pathways Project researchers have also analyzed model typologies. Electricity sector models 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. Energy system models In addition to the electricity sector, energy system models include the heat, gas, mobility, and other sectors as appropriate. 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. Producing hybrid top-down/bottom-up models to capture both the economics and the engineering has proved challenging. == Energy models ==