Two methods for decreasing the flexibility gap in national energy systems

Abstract

Abstract:More variable renewable energy sources and energy efficiency measures create an additional flexibility gap and require a novel energy planning method for sustainable national energy systems. The firstly presented method uses only EnergyPLAN tool in order to decrease the flexibility gap in a national energy system. Generic Optimization program (GenOpt®) is an optimization program for the minimization of a cost function that is evaluated by an external simulation program, such as EnergyPLAN, which was used as the second method in this research. Successful strategies to decrease the flexibility gap are verified on the case of the Serbian national energy system using two methods for its structure design: (1) the iterative method, based on heuristics and manual procedure of using only EnergyPLAN, and (2) the optimization method, based on soft-linking of EnergyPLAN with GenOpt®. The latter method, named EPOPT (EnergyPlan-genOPT), found the solution for the structure of the sustainable national energy system at the total cost of 8,190 M€, while the iterative method was only able to find solutions at the cost in the range of 8,251-8,598 M€ by targeting only one sustainability goal. The advantages of the EPOPT method are its accuracy, user-friendliness and minimal costs, are valuable for planners. Highlights Heuristic and optimization method for sustainable national energy system structure  The same input assumptions resulting in different energy system structure  Both methods are successful in decreasing of the flexibility gap  The EPOPT method advantages are in the speed, accuracy and planner comfort  Advanced method for the sustainable national energy policy planning

Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints

“…Such alternative models will show different sensitivities, depending on the specific modelling set-up (see for instance [45] for a discussion of the sensitivity to energy and emission market prices for an optimisation model for sustainable energy systems). For models applying heuristic solutions, the behaviour of the cost function around the optimum -which can be assessed using a multiparameter sensitivity analysis -will also have an influence on the necessity to adopt more elaborated but computationally expensive optimisation methods (see [46] for a comparison of heuristic with optimisation methods in the context of an energy planning tool for sustainable national energy systems). Nevertheless, the restriction to a single model, for which there is complete knowledge of the underlying data and control over all modelling details, allows to assess comprehensively the modelinherent sensitivities to input data and optimisation constraints.…”

Review of planning methodologies used for determination of optimal generation capacity mix: the cases of high shares of PV and wind

“…Müller in [56] argue that inclusion of flexibility in long-term planning studies is limited. Ignoring the inclusion of flexibility in expansion modelling results in expensive capacity plans as demonstrated in [43,57,58,82,[99][100][101][102][103].…”

“…Ramp requirements tell the system operator the megawatt per hour that is needed from the generation fleet. A plethora of modelling studies such as the one done in [11,43,57,58,82,[99][100][101][102][103] deal with flexibility by assessing the metrics in (1) and (2) and most of the analysis in these studies are done after optimising for capacity expansion. The main aim of these models is to check system adequacy (flexibility requirements for short to medium term) following capacity determination.…”

“…With increased penetration of intermittent renewable energy resources, there has been an increase in studies that look at voltage stability and frequency issues. These studies are highlighted in [39,[96][97][98][99][100][101][102]. Voltage stability and frequency control studies assess the voltage behaviour and the frequency control requirements [102].…”

A Transition Strategy from Fossil Fuels to Renewable Energy Sources in the Mexican Electricity System

“…• Biomass usage; [15] EnergyPLAN is combined with a multi objective evolutionary algorithm, and a study that links EnergyPLAN with an external cost optimization computational tool is found in [16]. Also, a study on a continental scale can be found in [17] where a scenario of 100% RES system for Europe is developed, identifying the necessity of a smart energy system for the entire Europe as the way to achieve it.…”