Chapter 1 Introduction
2.4 Addressing RE Intermittency in Energy system Mod- els
2.4.2 Exogenous Approaches for Methodological Improvement
2.4 Addressing RE Intermittency in Energy system Models 31 motive of these approaches is to endogenously simulate short-term power system operation within long-term planning models.
model. One way soft-linking of a system model namely Iris-TIMES and a production cost model PLEXOS is detailed in Figure 2.8 [15]. This particular analysis shows that, though optimized portfolio from the Irish-TIMES model is adequate, essential flexible elements, namely storage, gas based generators, and wind curtailment are undervalued while CO2 emission has been overestimated. Belgium-TIMES electricity sector planning model has been linked with a unit commitment model LUSYM. In this study, it has been observed that low level of operational detail leads to overestimating RE generation and underestimating operational costs; but these effects are only prominent at a high share of RE (35%–50%) [16]. A dispatch model namely highRES has been utilized with long-term planning model UKTM (UK TIMES model) to evaluate technical feasibility of energy system pathways.
Results show that the planning model favors base load capacity over flexible generation option, which leads to wind curtailment and load shedding in dispatch model [123, 124].
Transferring results from system model into an operational model is challenging due to the mismatch in spatial and temporal definitions. The task of constructing input data set for a production cost model, using the output from a system model, can be automated with the help of optimization tools [125].
Bidirectional linking methods: Among hybrid approaches, iterative methods are more popular than uni-directional ones. Here, after verification of system portfolio by operational model, information is fed back to the system model, and new solutions are attempted. Overall method of bi-directional soft-linking is elaborated in Figure 2.9. A multi-regional energy system model PERSEUS-RES-E and a dynamic dispatch simulation model AEOLIUS are connected via a hard link,i.e.each model generating its own output file [126–128]. Manual iteration is performed to analyze the differences in results after each data exchange and model run. Using the simulation results from the operational model, additional constraints representing reserve capacity, partial load operation, start-ups, and shut-downs, etc. are included in the system model for better presentation of RE intermittency.
A TIMES based energy system model and a probabilistic production simulation model ProPSim are connected via an iterative soft-link [26]. The methodology requires system model to run first and calculate RE penetration level. The probabilistic model then simulates system operation and calculates balancing capacity, and storage needed to support residual hourly load variation for that penetration level. These results with updated utilization factors are then fed back to TIMES model which then attempts a new solution. Convergence is achieved when there is no need for new investments for system balancing. MARKAL based long-term system model MARKAL-NL-UU has been soft-linked to unit commitment and economic dispatch (UCED) model REPOWERS [129, 27]. The long-term model calculates
2.4 Addressing RE Intermittency in Energy system Models 33
Year=Start
Year Start
Run Energy System Model
Run Operational
Model Solution
Optimal?
End Year?
End
Capacity, Generation
Update Energy System Model Parameters Current Resource
Stock, Future Demand, Technology Description, Policy
Year=Year+1
Generator Cost, Technical Characteristics
Time Series of demand, RE
generation forecast
No
Yes No
Yes
Figure 2.9Bi-directional iterative Soft-Linking methodology
technology capacity and serves input data to UCED model, which then optimizes hourly scheduling and dispatch of generation units over a whole year. Analyzing result from UCED model, actual reserve capacity and efficiency of generators are calculated and fed back to long-term model to obtain a new solution. Similar integrated modeling frameworks involving long-term optimization model, and hour-by-hour simulation model have been reported [17, 130]. The simulation model is used to evaluate the reliability of capacity portfolio obtained from the optimization model. The capacity is adjusted if it does not satisfy the simulation model.
Spatial Resolution Enhancement
Consideration of higher spatial definition in planning models is often prohibitive due to unavailability of data at desired resolution. Also, higher spatial resolution adopted in endogenous approaches does not allow model to address intra-regional RE variability. System models can utilize separate tools to model geographical RE variability at suitable scale, develop data, and incorporate them in planning models. Geographic Information System (GIS) tools are useful in this regard to develop realistic RE capacity and generation related potential. ReEDS (Regional Energy Deployment System) model endogenously considers
high spatial resolution [131]. The USA has been divided into five type of resource regions to account for geospatial differences in resource quality, transmission needs, and electrical political boundaries. Total transmission network of the USA has been represented by 134 nodes connected by 300 lines. Linearized DC-power flow has been considered to track power flow between regions. But, a major improvement in representing intra-regional spatial variation of RE sources is made exogenously using GIS tools. For each region and class of RE resource, new supply curves are developed to capture additional grid integration cost for connecting new RE plants to nearby transmission lines. Thus, geographical value of a particular site regarding resource quality is considered by this approach. This information is valuable as new RE capacity installation often takes place at remote areas associated with high integration cost which traditional models fail to consider.