Recent updates on energy planning models – a review

: Energy is the most important part for community and financial growth of any country. Energy planning models play a vital role in strategy formulation and power sector progress. In this paper a systematic review and comparison of energy planning models developed and applied from 1977-2019 has been presented. The review indicates that most of the energy planning models has been presented and applied in developed nations. Only few numbers of energy planning models have been presented and applied in under developing countries. The review also shows the comparison of energy planning models applied in developed and under-developed countries. This review article will assistance the energy managers, scholars and strategy makers broadly.


INTRODUCTION
It is very important to design energy as it is a crucial component for the progress of a nation. Appropriate planning is essential at the global and national level to manage energy requirement and consumption [1]. Energy security is a socio-economic and political aspect that promotes sustainable development (SD) in any country [2]. Recently, various contests have appeared in public such as variations in climate, security of energy consumption and requirement supply and financial decline. So, energy sector, particularly renewable energy is being managed to compete energy concerns. It is very important to build energy systems based on renewable energy. The renewable energy system can overcome pollution and improve the economy of a country [3]. Energy planning models support to forecast the effects and to evaluate the implications of energy strategy [4]. Various energy planning models have been established and used in various countries. Application of specific energy model depends on input and output data parameters, procedure and skills requirement. Some energy planning models are technically obvious and needed a large number of parameters, most of these are not freely accessible in developing nations.
The skills and software requirements for some energy planning models are too difficult for establishing and running of models. Mostly, energy planning models have been established in developed states to evaluate a particular matter. A very few number of energy planning models have been used in developing countries [5]. Though many reviews have been done in previous studies, comprising [6][7][8][9][10][11][12][13][14][15][16][17][18], but a comparative, state of the art study is hardly found in research. The objectives to conduct this review are (i) to give a comprehensive, state of the art and systematic review of energy planning models applied in developed and under-developed countries from 1977-2019 (ii) comparison of energy planning models presented in developed and under-developed countries of the world.

REVIEW OF LITERATURE
There are many revisions on energy planning models have been done in literature. Studies conducted in many countries including  confirm the importance of applications of energy planning models for the formulation of energy policy selections at various stages. Huge number of researchers have done the research to develop the integrated energy planning models. A systematic and comprehensive review of energy planning models has been introduced in this review paper. Landsberg [60] presented a review for energy policy modeling. Walter and Weyant [61] discussed the integrated energy modeling theory. Boyd et al. [62] illustrated a NAPAP Integrated Energy Model. Alam et al. [63] developed an energy planning model for integration of Bangladesh's rural energy systems. Werbos [64] compared the econometric models applied in energy planning modeling and engineering. Labys and Asano [65] discussed the process energy models for industrial applications. Poch and Jenkins [66] discussed the application of energy planning models by dynamic programming. Psarras et al. [67] introduced multiobjective programming techniques in large scale energy planning model. Labys et al. [68] studied numerous special energy planning models. Oliver [69] discussed the relationship between computer and mental energy planning models. Belyaev [70] applied pay-off matrix technique for the energy planning. Bowe et al. [71] presented the application of MARKOV energy planning model. Loagn [141][142][143][144][145][146][147][148]. There are also other energy planning models including LEAP, New Earth 21 and National Energy Modeling Systems which have been established and applied in many countries including [149][150][151][152][153][154][155][156]. The MARKAL energy model was applied in many states and regions for sustainable development including   [231] proposed an econometric energy model for Malaysia's economic policy and energy planning up to 2030. Du Can et al. [232] applied Leap model to estimate the energy consumption in households, industrial sectors, commercial and transport sectors worldwide. Melton [233] developed a hybrid energy-economy technique to mitigate the climate change in Africa, Middle East and Latin America. Blanco et al. [234] established a DSS energy model for micro-hydroelectricity plants. Frombo et al. [235] presented EDSS for energy planning of forestry biomass application. Khakbazan et al. [236] developed an experiment in Brandon, Manitoba for checking the influences of fertilizer organization on energy inputs and GHG emissions. Popescu et al. [237] proposed a simulation and forecast energy model for the consumption heat in buildings. Parikh and Ghosh [238] developed an energy model to model the national economic policy. Daniel et al. [239] developed a method for power system planning India. Drouet and Thénié [240] developed an ETEM Model to evaluate the policies for SD. Bujak [241] developed a model to find optimum energy consumption. Sharma and Bhattacharya [242] established LP energy model for long term generation expansion planning problems. Andreassi et al. [243] proposed an energy optimization model for the economics of electricity delivery arrangements. The HOMER model is applied for the design of hybrid systems by [244]. Lagorse et al. [245] presented multi agent approach for energy planning. An energy optimization model was developed by [246] for small scale power generation systems. Ehsani et al. [247] presented a MILP model for power production. Morais et al. [248] [285] combined DES and ESO models for a non-existing systems. Sarica et al. [286] applied an energy optimization method to explore dynamics of theoretically optimized energy sector. Bautista [287] applied the Leap energy model to analyze current and upcoming energy situation in Venezuela. Weijde and Hobbs [288] used a model to define the multilevel nature of energy transmission. Promjiraprawat and Limmeechokchai [289] applied a based scenario analysis method to evaluate Thailand's renewable energy policies. Comodi et al. [290] used TIMES energy model to assess local renewable energy policies for the town of Pesaro. Devogelaer et al. [291] applied TIMES energy framework for the Belgian renewable energy systems to recognize and discover pathways for 100% renewable energy achievement. Bauer et al. [292] developed REMID-R model for energy management. Amer et al. [293] mentioned that scenario energy planning stimulates tactical thinking. Hunter et al. [294] presented TEMOA energy planning framework for conducting energy systems analysis. Zhang et al. [295] applied a multi-period energy optimization model to estimate optimum ways electricity sector. Kannan and Turton [296] created STEM-E model originating from TIMES model for power systems. Cho et al. [297] presented a distribution algorithm that reduces overall budget of power application in buildings. Safaei et al. [298] established a model for optimization of cogeneration and solar energy structures. Pereira and Saraiva [299] proposed a long term GEP model. Kwon et al. [300] examined the generation expansion planning problems of South Korea by using MILP model.
In Tables 1 and 2 [307] utilized a model to find the optimum generation expansion planning of the Southeast Asian countries. Amorim et al. [308] applied TIMES model for electricity application in Portugal up to 2050. Poncelet et al. [309] utilized TIMES model to assess the effect of employing longterm models with low time-based resolution. Iqbal et al. [310] presented a comprehensive review energy optimization models for renewable energy sources. Halkos et al. [311] used Leap model to explore the national targets for energy and environmental related issues. Oscan et al. [312] coped Long term GEP of Turkey with integration of RES. By presenting an optimization energy model for African nations such as Ethiopia, [313] established other scenarios for the nation's energy demand up-to 2050. Prasad et al. [314] applied Leap energy model to discuss the prospective of biofuels in the transportation and power generation sector. Kale    Oree et al. [366] presented an assessment of generation expansion planning models. Moreira et al. [367] tested the energy co-optimization models. Emodi et al. [368] applied LEAP energy model in Nigeria to discover upcoming energy requirement and supply. Härtel et al. [369] proposed a Multi-Node transmission expansion planning model. Hemmati et al. [370] proposed an energy optimization model for optimum planning on RES. Nerini et al. [371] developed UK TIMES energy model to evaluate the UK's power systems. DeLlanoPaz et al. [372] presented a study on energy planning models. Ioannou et al. [373] provided a discussion of risk based energy models for sustainable power planning. Moradi et al. [374] presented a Single-node energy model for energy planning. Dhakouani [375] [394] presented a building energy model. Zatti et al. [395] presented a district and building models for energy planning. Zhang et al. [396] suggested a Single-node model. Ahmadi et al. [397] established an energy optimization model for Long term energy planning. Deveci and Güler [398] proposed CMOPSO model for RE planning. Koltsaklis and Dagoumas [399] presented a comprehensive study on GEP problems. Sadeghi et al. [400] discussed GEP problems. Komiyama and Fujii [401] inspected massive integrations of variable Renewable Energy Sources into electricity-generation of Japan. Aryandoust and Lilliestam [402] applied a bi-level optimization model for German upcoming electricity systems. Flores-Quiroz et al. [403] proposed a methodology to discuss GEP problems. Garcia-Herreros et al. [404] suggested a Mixed-Integer Bi-Level energy optimization model for planning. Collins et al. [405] conducted a discussion for integration of short-term fluctuations of power systems to energy models. Alizadeh et al. [406] presented a study on classification of latest energy mechanisms in electric systems. Papaefthymiou and Dragoon [407] discussed the critical stages for building energy systems. Mikkola and Lund [408] discussed optimum ways for the management of the renewable energy systems. Palmintier and Webster [409] inspected the effect of working flexibility on generation expansion planning. An approach is presented by [410] for balancing market expenditures and requirements. Ji et al. [411] discussed power systems with various RE portfolio standards and capacity requirement setups. An approach is presented by [412] to measure the impacts of variables and intermittent Renewable Energy Sources on Long-term energy optimization. Krishnan and Cole [413] applied a linear programming model to measure the impacts of spatial resolution of power systems. Poncelet et al. [414] discussed the generation expansion planning problems. Pereira et al. [415] presented an energy optimization-based GEP model to calculate impacts of variable Renewable Energy Sources on efficiency of thermal power plants. Lu [417] proposed the coordinated generation expansion planning under high air flow rate. Blanchard [418] discussed the upcoming development of Macro-Economic models, with particularly emphasis on CGE models. Rasouli and Teneketzis [419] developed an approach for generation expansion planning. A review has been conducted by [420] of decision making models. Saavedra et al. [421] presented a study of applications of Sustainable development approaches in RE. Gravelsins et al. [422] discussed that how SDM can be applied in modelling the power systems. Najafi et al. [423] applied a novel approach for medium-term management of an energy system. Guler et al. [424] constructed a theoretical framework of regional energy hub. Wang et al. [425] developed an automatic and linear modeling methodology for energy optimization hub systems. Mohammadi et al. [426] applied various concepts and energy models for energy hub.
Hemmati et al. [427] suggested a sustainable development structure for optimum design of energy hub systems. Zhan et al. [428] established a stochastic model for solving the energy problems. Zhan et al. [429] developed a bi-level energy model to resolve generation expansion planning problems. Resener et al. [430] conducted a study of energy models for the solution of energy planning problems. Zheng et al. [431] developed a robust MINLP energy model that improves the alignment, sizing and operation of CCHP system. Hemmati et al. [432] presented a two-level energy planning algorithm. Rad and Moravej applied genetic algorithm technique for generation expansion planning. da-Silva developed [434] MOEA technique for generation expansion planning. Rastgou and Moshtagh [435] introduced Firefly algorithm technique. Sisodia et al. [436] applied hybrid algorithm model for resolving TEP problems. Guerra et al. [437] developed a combined energy model to resolve GEP and TEP problems. Moradi et al. [438] developed an algorithm technique to resolve the TEP problems. A social-spider algorithm is used by [439] to solve TEP problems. Rathore and Roy [440] introduced a STEP energy approach to discuss the impacts of plug-in electronic vehicles. Du et al. [441] determined that the electronic system flexibility is to be overvalued if no or basic operation restrictions are measured. A Risk-Averse Stochastic programming model has been introduced by [442] for grid power management. Wu et al. [443] developed TEP model for renewable energy. Yeo and Lee [444] introduced a sequences of various energy planning approaches for urban energy planning. Lu et al. [445] developed an Interval-Fuzzy programming model to enhance China's power management system. Zhang et al. [446] proposed a Fuzzy-Stochastic system model for supporting SD and management of power systems. Gupta and Gupta [447] introduced a robust optimization energy model for micro grid power planning. Falke et al. [448] proposed a Multi-Objective energy optimization and simulation model for the construction of RES. Muller et al. [449] introduced of a methodical modeling technique for multi-modal energy systems planning. Ioannou et al. [450] reviewed the influence of multi-modal power systems modeling on distribution and transmission grids. Saboori and Hemmati [451] discussed the optimum planning of ESU's as a Mixed Integer and nonlinear energy optimization problems to increase the benefit of supply companies. Li et al. [452] established a two-stages energy optimization model to solve the problems of ESU's and DGs. Guerra et al. [453] proposed MILP model for GEP in an interconnected energy system. Min et al. [454] applied a Stochastic optimization model for Long-term capacity expansion planning of energy systems. Mahbub et al. [455] used a new methodology for Long-term power planning. Khan et al. [456] suggested a partial equilibrium linear model. Parkinson et al. [457] developed systems analysis model by applying Multi-Criteria investigation methodology. Lv et al. [458] proposed an energy optimization model for planning of water-energy system. Saif and Almansoori [459] developed a MILP energy model to enhance long term capacity expansion planning. Smaoui and Krichen [460] applied an energy simulation-based model for optimum energy planning. Hickman et al. [461] used a Mixed-Integer model for optimum working of a system. Tomar and Tiwari [462] applied HOMER energy model for economic analysis of grid-connected power systems. Yang et al. [463] presented a review to address the sizing of battery for renewable energy system. A review has been conducted by [464] on existing models for the optimum design of PV-battery systems. Huang et al. [465] developed a Mathematical Decision-Making model for ideal storage ability in grid connected PV power system. Sani Hassan et al. [466] proposed MILP model linked with DER-CAM model to define maximum power flows. Grover-Silva et al. [467] applied power flow distribution grid planning model to address sizing and placement of supply grid connected battery system. Ehsan and Yang [468] established a distributed GEP model to decrease power losses. Ehsan et al. [469] proposed distributed generation investment model to enhance the value of power in distribution system. The investment planning model developed by Ehsan and Yang [470] uses DGP and storage arbitrage advantage to enhance distribution system operator's benefit. Ehsan and Yang [471] developed a MMIP energy model for energy planning. A particle swarm optimization model introduced by Hemmeti et al. in [472]. A Harmony Search Algorithm technique is in [473] for energy planning. Zhang [474] developed a Non-Dominated Genetic Algorithm Approach for energy planning. Zhang et al. [475] used a hybrid technique for energy planning. A Multi-Stage Long-term distribution planning model was proposed in [476,477]. A Multistage distribution model considering energy storage measures was developed by Shen et al. [478]. A bi-level energy model was introduced by Asensio et al. [479]. A Two-Stage Robust programming model was developed by Amjady et al. [480]. Ahmadigorji et al. [481] suggested a reinforcement GEP model. A MILP model was presented by Arias et al. [482]. A second-order conic chance-constrained energy model was proposed by [483]. Ortiz et al. [484] applied a MICP energy model for energy planning. An integrated energy planning model was developed by [485]. A deterministic energy model was introduced by [486]. A MILP energy model was presented by [487]. Dominguez et al. [488] proposed a robust two-stages short-term energy planning model. Lipu et al. [489] used HOMER for sensitivity investigation on hybrid renewable energy system. A stochastic mixed-integer convex programming model has been suggested by Home-Ortiz et al. [490]. Peerapong et al. [491] applied HOMER energy model to maximize the electrification development in diesel based generator in Thailand. Soukeyna et al. [492] utilized HOMER energy model to achieve a viability study on generating energy from hybrid renewable system. Sadati et al. [493] used HOMER energy model for energy planning in Mediterranean area. Hantoro et al. [494] presented HOMER energy model to discuss the energy requirement and design of hybrid systems in Indonesia. Qolipour et al. [495] applied HOMER and MATLAB models to suggest a mathematical energy model for improving the renewable electricity expenses.

CONCLUSIONS
The different energy optimization models presented/applied in developed and under-devolved countries have been reviewed. The review show that large number of models have been presented and applied in developed nations like USA, China, Italy, Brazil, Portugal, Netherland, Japan, Sweden, Spain, France, Denmark, Germany, Belgium, Taiwan and Switzerland. Only a few numbers of energy planning models have been used in under-developed countries like India, Iran, Turkey, Malaysia, Iraq Saudi Arabia, Bangladesh and Pakistan. For sustainable development and renewable energy concerns, major factors for the application of energy planning models are life span of the energy system, consistency, alternating supply, geographical location, investment and community involvement.