International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015
ISSN: 2319:2682
State of the art on modelling techniques for renewable energy integration into the energy mix Dr. E. Tora* Researcher /Department of Chemical Engineering and Pilot Plant, National Research Centre, El Giza, Egypt
[email protected];
[email protected] Dr.W. Niekerk Professor/Centre for Renewable and Sustainable Energy Studies, Stellenbosch University, Cape, South Africa
E. Fouche Stellenbosch University, Cape, South Africa
[email protected] Dr.A. Brent Professor/Centre for Renewable and Sustainable Energy Studies, Stellenbosch University, Cape, South Africa
[email protected]
[email protected] Abstract— Energy demand is on the increase which results in a
Index terms - Renewable energy integration; national energy mix,
growing need to integrate renewable and sustainable energy
modelling; free computer tools
sources into the national energy mix. This demand is subject to
I. INTRODUCTION
trade-off due to interacting economical, technical, social, and
Energy is an indispensable requirement for continuous development, thus there is a continuous demand for energy. Attention is paid to renewable and sustainable energy sources such as solar and wind energy as alternative or supportive energy resources. According to the International Panel on Climate Change [1] [2] [3] this trend is stimulated by concern about the environment and the need for sustainable and clean energy sources. The employment of renewable energy sources gives rise to several challenges and questions that need to be investigated carefully to ensure a stable power supply as well as cost reduction [4] [5] [6] [7]. The solutions to these problems are distributed over stages briefly described as planning for new power plants, capacity-expansion, and the operation of the plants. Planning for new solar energy plants as well as capacity-expansion of existing conventional power plants via integrating renewable energy require spatial and temporal studies; the reliable operation of the plants requires an increase in unit capacity , a generation schedule, units commitment and an increase in electricity quantity. A tool is needed to investigate integrating renewable energy into the national electricity grid and attention should be paid to the above-mentioned pertinent issues. Modelling is approved by the different sectors and stakeholders as an effective tool. A very large number of models exist [8] [9] [10] [11] [12] but under different classifications and subtypes and these are described in a number of review articles. These above-mentioned articles all revolve around energy modelling but with different titles and classifications which include energy system models in energy and climate change [13] energy models [14], system dynamic models for the electricity market [15], and electricity market modelling [9].
environmental aspects. Energy integration projects can be carried out via certain computer tools that vary with regard to the investigated aspects. In this paper, a review of the integration of renewable and sustainable energy is given, based on the principle investigated criteria along with defining the most appropriate free computer tools that can be used to implement such projects. Foundations are also laid to enable the further development of tailored tools. To achieve this objective, three approaches (single, hybrid, and multi-criteria approaches) are highlighted; and four pillars are defined as the main pillars. Therefore, some appropriate free computer tools are listed, and their fundamental scientific characteristics are analyzed. The objective is to conclude a defined and free of charge route to accomplish renewable energy integration projects. It is found that the objective is achievable, but there is no tool that can accomplish all the
required tasks, However it is possible to
develop tailored tools by means of knowledge of the scientific background and the determination of the key aspects of the integration case. Free tools can do good work and facilitate the integration of renewable energy systems and therefore eliminate the need of complicated modelling.
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015
Sustainable Energy System Technology
Environme nt
Society
II. GENERAL CLASSIFICATION OF MODELS Regarding the modelling of the integration of renewable and sustainable energy, there is a very large number of models which makes it a tedious task to choose which fits a given situation best. As pointed out in Section 1, much effort has been put into classifying these models in order to facilitate this task. Despite these classifications, it is still a difficult task to determine which model best satisfies the demands of your study or project, or in other words which model should be used to solve a given problem. . Any model is originally used to analyze a certain energy system from the view point of economics, technology, society, or the environment, which are the main pillars for sustainable design of energy systems as shown in Fig. 1 [18] [19], The authors of this paper prefer classifying the models based on the objective
of the model and the consideration of certain criteria. This classification makes it easy to choose the most appropriate model and tool to conduct a modelling study of the energy system. This approach does not require much background knowledge since it is straightforward method.
Economy
Based on the interaction between the various sectors there are two approaches namely top-down and bottom-up approaches [16]. In one review article the focus is on energy market types and the mathematical structure of modelling techniques, and the models are divided into three categories: equilibrium models, simulation models, and optimization models [9]. While addressing the optimization models, the writer refers only to single objective optimization and dismisses multi-objective optimization. Very recently multicriteria optimization has been reviewed separately [16]. In another paper the models are categorised into seven main types which are planning models, supply-demand models, forecasting models, renewable energy (solar, wind, biomass and bioenergy) models, emission reduction models and optimization models [14]. But these types are interchangeable, and sometimes interact or overlap. Recently the high penetration of renewable energy into the grid and carbon reduction has been investigated [17]. In other cases the focus is on one or more specific type of modelling technique. It is a tedious task to go through all these review articles and understand the included concepts and techniques, especially for stakeholders who have limited modelling background. Furthermore, the facts are confusing and it is difficult to choose a model which is suitable for a particular situation. Therefore this paper represents an attempt to present a brief and simplified description of all the related and interacting aspects and how they are linked. This should make it easier for the stakeholders to understand the approaches and the fundamentals. The paper is organized as follows: In Section 1 there is an introduction, objectives are discussed and the paper is compared with other preceding review papers; ; In Section 2 a general classification is introduced and there is a definition of o an appropriate computer tool to achieve the objective ; In Section 3 the existing models and tools are categorized and examples are given; In Section 4 some of the relevant fundamentals of these models and tools are explained ; and finally in Section 5 brief conclusions are listed and the most important outcomes are highlighted. .
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Fig.1 Four pillars of sustainable energy system
A detailed economic analysis of energy systems can be conducted via bottom-up models but this entails ignoring the detailed technologies involved, the opposite is true of the up-down models [20]. To solve this problem, based on the insights of [21], combining these two aspects (technologies and economies) by means of hybrid models is considered; however the merits of what are called multi-criteria models have been introduced earlier on [22] [23]. These models involve three or four of the main aspects of sustainable design. Hence, in this study we tend towards organizing the renewable energy integration models into three approaches and four pillars. The approach refers to the way and order of processing the information in order to achieve a certain goal. The pillars refer to the main strategic aspects affecting the energy system and each pillar is an umbrella for many factors and variables. These three approaches are single-, hybrid-, and multi-objective; the single-objective model is subdivided into top-down and bottom-up. The objective is the criterion considered in the model to be analyzed or optimized. The four pillars are economy, technology, environment, and society. These pillars can be found in other papers where they are sometimes called criteria. These three approaches and four pillars, along with links representing their interaction, are presented in visual form in Fig. 2.
Fig. 2 Approaches to and pillars of renewable energy integration models
When two of these pillars are combined, the result is a hybrid model. . When a model investigates more than two of the above mentioned pillars, a multi-objective approach is applied. In the next section (Sec.3) the models described in the literature are organized and reviewed. This means that the model along with the proper computer tool that can be used
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015 for implementation of the study is reviewed. These models and tools are listed in concise tables to enable stakeholders to select the most appropriate model. III. CLASSIFICATION OF MODELS The reviewed models are organized and divided into specific categories in accordance with the three approaches and four pillars described in Sec. 1. In each category there are number of known models along with the computer tools most likely to be appropriate for the implementation and solution of the modelling process. A. Single-Objective Models (Bottom-Up/Top-Down) The single objective model focuses on the objective which may be minimizing the energy system cost, reducing the greenhouse gas emission, society satisfaction, or the reliability of the technology. According to the common definition for energy modelling [21] [23], the top-down approach is basically used for economic (Eco) analysis since it includes macroeconomic consequences and some microeconomics, and the bottom–up approach is used for detailed technological (Tec) analysis. Nevertheless, this definition is extended here to cover any model with a single objective. When the detailed subsystems and elements are known, and they go through a combination process to achieve an objective, this is called the bottom-up approach. When the overall picture is known and the bottom elements are specified but not detailed, the top-down approach is applicable, as shown in Fig. 3.
Fig.3 The difference between top-down and bottom-up modelling approaches
In Fig. 3 the top box refers to the overall system (the electricity portfolio with renewable energy integration) which is going to be treated mathematically in a model to achieve a certain target (objective) as stated above. The purple boxes indicate intermediate sub-systems and pertinent information; the different colored boxes at the bottom stand for the first level of information and the basic elements of the system. For example, when the intermediate system includes solar energy, the bottom boxes represent types of photovoltaic cells and thermal solar collectors. Bottom-up optimization models use the linear programming technique to determine the least net present cost supply to meet a given energy demand. The most widely used model, of this kind, is the Market Allocation Model (MARKAL) which models the entire energy system from energy resources to end users through energy conversion
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processes [22]. The building blocks of the MARKAL model include resources such as import, mining and export resources; processes such as refineries, fuel processing and emissions control, heat- and electricity generation; and energy services such as end-use and devices. The model has been expanded and there is now a family of MARKAL models. The model can also be used to investigate environmental effects and one of its major new developments is that energy demand can be price-responsive. B. Hybrid Objective Models
Hybrid models refer to the models that combine the aspects of both bottom-up and the updown models/approaches in one model [21]. However, this concept ―hybrid model‖ is extended herein as shown in Fig.2 to include any model combining two of the main aspects of interest: technology, economy, society, or environment. Three main hybrid approaches have been developed: i) a ―soft-link‖ between two independent top-down and bottom-up models through an interactive process among inputs and outputs; ii) linking one model to a reduced form of the other, usually linking a bottom-up model to a simplified CGE model; iii) Mixed Complementarity Problem (MCP) format joining the two modelling forms in a single integrated model, and introducing technological detail in general equilibrium models [24]. A techno-economic model has been developed to analyse the stand-alone hybrid renewable power system, and it has been used for Ras Musherib in the United Arab Emirates [25]. Based on techno-economic analysis, shift to renewable energy is foreseen but that this is dependent on discontinuing the use of fossil fuel [26]. Simulation and optimization computer tools for performing single and hybrid modelling are already available. The advantage of these software tools is that they are easy to use and get results fast. These tools have been reviewed by a number of researchers such as Connolly et al. [27] who gives a detailed description of 37 tools. Hence none of these details are repeated here, but they are classified here as a single or hybrid-objective tool in order to provide the stakeholder with a list of software tools from which he can select suitable software for the objective of interest. Only software tools available free-of-charge is considered, more specifically the 10 most used free tools. Table 1 briefly indicates the objective of each tool and if it is basically a single- or hybrid objective simulation or optimization tool. EnergyPLAN is a computer model, selected by Connolly et al. [27] to model the existing Irish energy system in order to identify future energy cost and the maximum wind penetration available. The model was firstly selected, due to
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015 the fact that the three primary sectors of any national energy system are considered, namely electricity, heat and transport. Secondly, EnergyPLAN has been proven to be efficient with the modelling of similar research conducted. This research includes the analyses of the effects of large wind penetrations, the optimum combination of various renewable energy technologies and the benefits of energy storage. EnergyPlan has also been used to analyze the energy systems of developed countries such as Spain, Denmark, Scotland, Poland, Germany and Estonia. Aalborg University in Denmark developed the EnergyPLAN model with the first version implemented in 1999. . The model has been improved and expanded since then (now there is a Version 11.0) into a comprehensive technological simulation model which includes possibilities for analysing different trade options, electricity storage and conversion possibilities, renewable units, nuclear power and hydro power with water storage, reversible pump facilities, different transport options, different heating options, a detailed model of compressed air energy storage, the option to calculate total annual social economic costs, waste-to-energy technologies in combination with geothermal and absorption heat pumps, facilities of Pump-Hydro-Energy-Storage, various Biomass conversion technologies, biomass to gas conversion technologies and the inclusion of V2Gs, transmission lines as part of the stabilization options, hourly balancing of the electricity system, etc. Furthermore, the model is user-friendly and the most significant improvement in Version 11.0 is the new appearance. The EnergyPLAN model is an open source and free of charge (EnergyPlan, 2014). The Renewable Energy Model-Deutschland (REMod-D) developed by the Fraunhofer Institute for Solar Energy Systems (ISE) models a possible future German energy system for all sectors, including renewable energy converters, storage components, secondary energy converters, etc [38]. The model includes energy fluxes (electricity, heat, fuel, hydrogen and methane) for all sectors, namely private households, the tertiary sector, industry and transport. It is a combination of the detailed modelling of the building sector in a complex energy system and a total cost optimization of a complex future energy system. The model is claimed to be an improvement of previous energy models such as MARKEL, EnergyPLAN model, IKARUS and PERSUS, where the gap is filled by the inclusion of detail in the building and electricity sector and where the costs of retrofitting buildings for increased energy efficiency are included. The National Research Energy Laboratory (NREL), the national laboratory of the US Department of Energy, and the Global RE Opportunity Tool (REOpp) were launched in 2013 to assist policymakers and analysts in understanding the size and location of market opportunities for cost-effective deployment of renewable technologies. The Beta version enables the analysis and visualization of the technical and economic potential of solar electric technologies ranging from residential rooftop systems to utility-scale installations. The tool is currently being developed as a web-based GIS
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application for evaluating opportunities for increased solar deployment [39]. Table 1 Single and hybrid-objective computer tools for renewable energy integration. Software
Type (n-objective) single hybrid
EnergyPLAN [28]
RETScreen [29] HOMER [30]
BCHP [31] Invert [32]
ORCED [33]
ENPEPBALANCE [34]
COMPOS [35] IKARUS [36] SIVAEL [37]
objective
Simulation
Optimization
Technoeconomic "energy portfolio" Economic
Technoeconomic economic Technoeconomic "energy portfolio Technoeconomic "operationelectricity dispatch" Technoeconomic "energy portfolio" Technoeconomic Economic
Technoeconomic
C. Multi-Objective Models A well-developed model does not ignore the relevant aspects, but it is designed to enhance them all. This is because energy has an influence on different aspects of life and is simultaneously affected by them [40] [41]. These authors refer to models which consider multiple pillars simultaneously. For example, a model tackles the economic, environment, and social aspects when analysing an energy system. That is similar to the known multi-criteria models. Models investigating these criteria with regard to competing sites for hosting renewable energy plants are known as spatial-temporal models. Technologies which take into consideration the environmental and economic aspects are necessary to set up and implement future energy plans. Furthermore, social aspects may have a role but the level varies regionally. San Cristóbal [42] stresses the significance of the multi-criteria approach, since the multi-criteria modelling approach is capable of putting these different and conflicting criteria together and investigating their joint impact [43] [44]. Multicriteria models can be formulated either as multi-criteria models or as multi-variable models. In the former the criteria themselves are subject to optimization, while in the latter
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015 different options are assessed and ranked in respect to these criteria [45] [46]. There are a number of ways for performing multicriteria models. These include AHP/ANP [47] [48] [49] [50], PROMETHEE [51] [52], ELECTRE [53], TOPSIS [54] [55] [56], MAUT [42] VICOR [57]; for more details refer to [40] [58]. Thus far the most used model is AHP [58] [59], and its generalized method ANP [60]. AHP stands for analytical hierarchy process while ANP refers to analytical network process. Both of these have a hierarchy decomposition structure which functions according to the top-down approach. However AHP offers unidirectional relationships between the criteria and the alternatives, ANP is composed of a network connecting all the criteria and elements and even allowing loops and aggregation into clusters as shown in Fig. 4. The top layer in both methods refers to the target of the model, which is followed by the criteria (C) layer and finally by the investigated options or alternatives (A).
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cautioned that examining only one technology without considering the context of the whole electricity generating sector carries the risk of ignoring some interesting alternative solutions, potentially leading to lower generation cost and, therefore, preventing the maximization of benefits. The German Aerospace Center (DLR) has released a free version of the simulation program Greenius, namely FreeGeenius, for the assessment of renewable power plant projects [67]. The program brings together technical and commercial aspects and can determine the yield of a renewable power plant at a specific location. It can also provide guidelines in terms of the design and construction of the power plant to ensure that the targeted quantity of power is fed into the grid. The model offers a combination of fast technical performance calculations, economical calculations and user interfaces for model parameter manipulation and analysis of the results. Third party meteorological data and performance maps generated with other software tools may be integrated easily. Thus, system planners and investors are provided with a preliminary overview of whether, and under which conditions, a solar thermal power plant, a wind turbine or a photovoltaic power plant will be best suited to the location in question [68]. Table 2 Multi-criteria (multi-objective) model applications in renewable energy systems.
Fig. 4 Difference between AHP (right) and ANP (left) methods for MCM.
The five indicators, ―sustainability indicators‖, to assess the energy system in terms of the four criteria which are economic, energy resource, society, and environment have been introduced [61]. These indicators are energy system efficiency, installation cost ($/kW), electricity price (c/kWh), emissions (kg CO2/kWh), and area (km2/kW). They represent the numerical values; however, the quantitative information is represented by constraints. Weights vector represent the priorities of the different criteria are used in a linear function that aggregates the different options. The uncertainty is reflected by introducing ―probability of dominancy‖ where the lower the probability value, the higher the uncertainty. Study introduced five sustainability indicators; however general sustainability indicators have recently been developed and reported [62]. Likewise, power supply technologies assessment using multi-criteria models has been carried out for Switzerland [63]. Particle swarm optimization (PSO) has been applied to minimize the cost of hybrid energy systems and reduce the unmet load and the greenhouse gas emission [64]. The model is solved with the ε-constraint method. ANP with BOCR (Benefits, Opportunities, Costs and Risks) have been compiled to perform analysis to determine to prioritize five renewable energy sources in order to determine the optimum for Turkey [65]. There is a growing application of multicriteria models in the field of renewable energy integration, thus to facilitate reporting most of these studies are briefly presented in Table 2. Individual power-plant strategies have also been the focus of extensive research, such as in [66]. It is however
Tool General Index of Sustainability [65] ELECTRE [68] ELECTRE-Fuzzy set [69] Value trees [70] ANP [47] ANP [71] AHP-value tree [72] AHP [46] VIKOR [42] MACBETH and a cost–benefit analysis [73] Sensitivity analysis [74] [75] New approach allowing stakeholders engagement [67] REGIME [77]
ELECTRE-III [69] Fuzzy Approximation design and fuzzy analytical hierarchy [78] AHP – SIMUS [79] AHP – ANP [80] TOPSIS-fuzzy set [81] AHP [82]
Outcome Random weight-coefficient vector probability of dominance Trade-off in regional energy options RE contribution strategic plan
And
Develop criteria to assess RE-Germany Optimum alternative technology – electricity generation sector of Turkey Evaluation of energy resources Energy policy - Finland Policy for energy conservation- Jordan Best project to provide 12% renewable energy in 2010 - Spain Assessment of different scale technologies - UK
Compare power plants with using RE and fossil fuel Evaluate future plans and decision along with RE alternatives - Greece Optimal energy mix of different renewable energyThassos- Greece Evaluate RE expansion – Island of Sardinia Determine optimum RE – Turkey
Ranking alternative energy – Southern Ontario, Canada Sustainable capacity building Select optimum PV technologies Best locations of new plants
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015 AHP-SUREDSS [83] ELECTRE IIIPROMETHEE [84] Computational approach [85] Based on AHP [86] AHP [16] PSO [1] AHP and SIMUS [65] AHP [87] CEM [88]
Optimum energy options for local demand of Colombia Energy planning via projects prioritization – Armenia Feasibility of power plant expansion Optimum locations- China Optimal mix for electricity generation considering 11 options Optimum hybrid system ranking renewable energy sources for Ontario Canada Optimum sources for Malaysia Evaluate ten power technologies and seven heat technologies using renewable energy
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represented by risk indicators such as VaR along with applying simulation model as introduced by [93]. The objective of Cournot competition is maximizing the profit of all the competing companies simultaneously. The price is estimated by the demand function. Uncertainty can be included. To represent these concepts mathematically, a simple mathematical general formula to represent this concept is as follows:
Max Pi
i {1, n}
Pi S i .Ci Ci f ( Di )
(1) (2) (3)
n
IV. FUNDAMENTALS The above-mentioned approaches and the computer tools used to analyse energy integration, all have their embedded built-in mathematical equations and formulas. Understanding the scientific foundations of these models may not be an essential requirement for some users, but may be useful in other cases. For instance, if an available computer tool does not fit in with the proposed model, the users might want to build their own system, users then can make use of other programs such as Matlab. In these circumstances, knowing the scientific foundation helps in the developing and solving of appropriate models. Developing the model is not an easy task, but on the other hand the developed model can be designed to exactly match the developer’s interest. The scientific structure as well as the modelling techniques is addressed by some review articles. Thus details are not given here, but only the basic concepts and techniques are pointed to, for more details refer to [1] [14] [16]. There are two key techniques: equilibrium models, and optimization models. These models can be developed by researchers or use can be made of models already embedded in available computer tools. Equilibrium models are based on the Nash Equilibrium concept of the markets [87]. It includes two subclasses: Cournot Competition which is a set of algebraic equations and Supply Function Equilibrium (SFE) that is a set of differential equations [89]. One of the related known applications is electricity generation capacity expansion where two very well-known standard models of electricity generation are formulated to tackle this target: the first is the ―Transshipment model‖ that consists of only power flow conservation equations and can be solved by employing the variation inequality (VI) technique [90] [91]; the second is the ―DC model‖ that contains Kirchoff laws along with the load conservation equations, and its solution is based on Complementarily problems (LCP) technique. Furthermore, uncertainty – such as in the growth of demand and energy resource availability or adequacy, and fossil fuel cost – is considered via different techniques. These include combining Cournot Competition and Dynamic Programming [92], and dealing with the uncertainty as a risk factor
S
S
i
(4)
i 1 m
D
D
j
j 1
SD D j f ( ) S i f ( )
j {1, m}
(5) (6) (7) (8)
In these general formulas, P refers to the profit, C is the electricity price, S denotes the supply (generated electricity quantity), D is the demand of the customers. The sub index i is referring to the generator or a firm and its value can be 1, 2,..,n which is the number of the participating firms or generators. Similarly, j is an index representing the number of customers who cause demand, and it can equal 1, 2,…, m is the number of the consumers (electricity demand) while and imply the uncertainty in the demand and the supply respectively. The second subclass which is the SFE represents different quantities of electricity which the electricity firms can offer at certain prices [94]. Thus the result i several residual demand curves, however it is not necessary to know these at the beginning. Linear or affine SFE is preferable due to the fact that it is easy to solve [95] [39]; SFE can account for the existing constraints on capacity, time horizon, and price. However solving the SFE model is still a challenge. Sometimes even finding distinguishing feasible and unfeasible solutions has to take place via iteration especially with the emerging or decreasing supply function [96]. The TIMES model, an integration between MARKAL and EFOM, has been developed in the framework of the Energy Technology Systems Analysis Program (ETSAP) implementing agreement of the International Energy Agency (IEA)This technology rich optimization model is not only the least costly system, but it also takes investment and operating decisions into considerations. The model considers the surplus from consumers and producers which leads to a partial equilibrium model. It is a multi-period model, which
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015 can be applied to a large number of regions and can capture trading options. It can solve for a number of constraints including emission constraints, by sector, for the whole economy or cumulatively over a period. It can also be used to identify more complex consequences due to mitigation actions [97]. Instead of solving a formal mathematical problem as in the equilibrium models, an iteration procedure is done, maybe in more than one step, to find an optimal solution this is known as simulation. Simulation models can be staticor adaptive based-agent models The static models make use of historical data with regard to demand and resources uncertainty while the adaptive models ignore the accumulated past experience [98]. Yet all simulation models are founded on the equilibrium concepts. For example, OteroNovas et al. [93] used Cournot competition to investigate different energy sources (thermal and hydro thermal) and their technical limitations in a simulation model, but solved the model by a two-step iterative procedure rather than using formal algebraic solving techniques, in order to maximize the profit. Similarly Vogstad [98] developed a simulation model comparable to SFE to construct optimal supply function implying real marginal cost and considering uncertainty about demand. Solving that simulation model yielded results comparable to those of the corresponding SFE, but the solution was easier to reach. . These are quantitative models with possible qualitative issues simulating the system variables’ change in time through differential equations; these variables can be stochastic [99] [100] [101]. The most important relationships to be simulated along within the boundaries of the system are selected iteratively [102] [103]. The models can be aggregated or detailed [104]. DS has superior merits such as the capability to include the uncertainty in future demand and price - as opposed to optimizations that assume perfect foresight [105], and comprises an aggregate level of detail and incorporates qualitative effects via casual relationships [106]. SD models can be classified according to the market types namely regulated markets and liberalized markets. The models of the first type are the foundations for the deregulated markets. For regulated markets, SD models have been developed to perform certain estimations [107] [108] [109] such as analysing energy efficiency, environmental policies, operational stability, production capacity expansion, and electricity prices. Optimization modelling tackles maximizing certain objectives (criteria) such as the profit which is basically controlled by the market type. This takes place under certain operational technical and economic financial constraints. There are two types of markets: regulated markets and de-regulated markets (liberalized markets). In regulated markets the electricity firm has no influence on the price as it is imposed from above, externally, thus this type of the model is known as ―exogenous price‖. Consequently the price has a definite fixed value in deterministic cases, or has a dimensionless probability
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distribution formula in stochastic cases where uncertain production (supply) profile and uncertain demand curve are considered. Hence the profit of the firm which is the product of the exogenous price times the production amount or curve is a linear function. Therefore, linear programming (LP) and mixed integer linear programming (MILP) are appropriate solving techniques. In de-regulated markets (liberalized markets) the electricity firms’ influences on the electricity price create competition among the different participants in the market. Thus each firm has its own price that is usually given as a function of the demand. If this demand along with the firm’s supply is treated as a certain definite value, a deterministic model is applicable. Otherwise, when considering the uncertain supply and demand curve in the residual-demand function via including a probability distribution, a stochastic model is applicable. In the two subcases of the de-regulated market, a quadratic function is likely to represent the profit since both the price and the production are expressed by functions. Nevertheless the solving method differs: in deterministic cases different techniques such as stepwise linearization and then LP or MILP are used; in stochastic models different approaches are employed such as dynamic programming [107], conditional value at risk (CVaR) [13] [108], Bender decomposition [110]. As stated, the optimization models are applicable to regulated markets in order to maximize the profit of one firm. Depending on the electricity price, two subclasses of models are recognized. Price can be an exogenous variable (either with fixed value or uncertain value represented probabilistically) or a function of the demand (either definite linear function or uncertain two curves representing the uncertain supply and demand). Linear optimization techniques are used to solve the exogenous subclass, but the demand function subclass is solved by dynamic modelling and other special techniques. For these two subclasses (exogenous price – demand function price), the mathematical optimization formulas can be written as in Fig.5.
Fig.5 General mathematical optimization formulas for these two subclasses: exogenous price – demand function price
V. CONCLUSIONS The integration of renewable energy sources are reviewed in terms of the pertinent approaches, namely, single-, hybrid-, and multi-objective or criteria, pillars (economy, technology,
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International Journal of Advanced Information Science and Technology (IJAIST) Vol.40, No.40, August 2015 society, and environment), computer tools, and basic scientific foundations There are number of defined indicators that can represent the different qualitative and quantitative energy related criteria and information. The review is developed in a way that makes it easy to determine the best computer tool to use, or own models can be developed; this is through presetting the objective(s) of the study and pre-defining the considered pillars. The main outcomes can be briefly given as follows: 1. Regional constraints always impose themselves; what is good for Europe may not be good for Africa. This is because of the social circumstances along with the differences in the environments; technologies can be imported but the social and environmental aspects cannot be easily replaced. 2. Different types of information and criteria (qualitative and quantitative) can be represented in a model via selecting the appropriate indicators. 3. A single-criterion model may ease the computational framework, but the more criteria involved, the more realistic the model. 4. Whether the results of a model are good or not depend on the user inputs of information, constraints, limits of modeled and optimized space, and assumptions. The level of sensitivity of the model depends on these inputs, and dependence on them varies but always exists. 5. There are software tools available to help stakeholders to assess and optimize their energy systems while considering a variety of pertinent targets. Using them is easier than developing a model from scratch, but almost none of these tools can perform all the tasks. In some cases it is better to develop a model which matches the developer’s interests and needs. 6. The econometric approaches require different sets of information that are not always available in developing countries, and therefore this type of modelling approach is not recommended for such countries.
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A. Brent currently he is appointed at
Stellenbosch University as a professor of engineering management and sustainable systems in the Department of Industrial Engineering (IE), and as the associate director of the Centre for Renewable and Sustainable Energy Studies (CRSES).
Authors Profile E. Tora attained PhD from Texas A&M University, USA in 2010; her PhD was about energy resources integration along with combined power, heat and cooling production optimization. She is a researcher (Assistant research professor) at the National Research Centre NRC of Egypt since 2011. For a year (August 2013 – July 2014) she also worked at the Centre for Renewable and Sustainable Energy Studies, South Africa. After that she worked as a postdoctoral researcher at the Future Energy FE Research Centre, Sweden till July 2015. Her research interest includes technical and economical aspects of energy systems. Likewise she conducts modeling, simulation and optimization of integrated energy systems, with seeking to design thermal energy systems with enhanced thermal and environmental efficiency. E. Fouche holds a BSc in Industrial W. Niekerk received Engineering and athe PhD MSc PhD in from the Technology Management from University at Berkeley UniversityofofCalifornia Pretoria. Recently she inisMechanical Engineering in 1994. a full-time PhD student at Currently and the Industrialhe is a professor Engineering, director of the Centre for Renewable Stellenbosch University, South and Sustainable Energy Studies at Africa. Stellenbosch University. His research interest includes renewable and sustainable energy.
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