Power Generation and Cogeneration Management Algorithm ... - Core

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ScienceDirect Energy Procedia 74 (2015) 1394 – 1401

International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES15

Power Generation and Cogeneration Management Algorithm with Renewable Energy Integration Joseph AL ASMARa,c,*, Raed KOUTAb, Kabalan CHACCOURc, Joseph EL ASSADd, Salah LAGHROUCHEa, Elie EIDc, Maxime WACKa a

OPERA/FRCNRS FCLAB 3539 Laboratory, University of Technology of Belfort-Montbéliard, France b IRTES-M3M Laboratory, University of Technology of Belfort-Montbéliard, France c Faculty of Engineering, Antonine University, Lebanon d Lebanese Center for Energy Conservation, Lebanon

Abstract Renewable energy and cogeneration systems integration into a grid has become an important issue for the power generation management. In this work, a new algorithm is proposed to manage the power generation. This algorithm takes into account the electrical and thermal load demands and the instantaneous renewable energy generation and cogeneration integration into a grid. In addition, the conventional power generators and cogeneration systems will be selected optimally according to economic and environmental criteria, and automated through microcontrollers. The reduced cost and pollution due to the integration of renewable energy and cogeneration sources are then calculated for decision making reasons. © 2015 2015The TheAuthors. Authors. Published by Elsevier © Published by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD) Keywords: Renewable energy; smart-grid; cogeneration; energy management

* Corresponding author. Tel.: 961-3-751589. E-mail address: [email protected]

1876-6102 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4 .0/). Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD) doi:10.1016/j.egypro.2015.07.785

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1. Introduction The integration of renewable energy (RE) and cogeneration systems (CS) in a power grid is becoming an important issue for power generation management. This paper proposes a novel algorithm to manage the optimized electrical and thermal production according to the demanded power. The main goal is to monitor and simulate the power grid taking into consideration the instantaneous production of RE and CS7RRXUNQRZOHGJHWRGD\¶VHQHUJ\LQWHJUDWLRQDSSOLFDWLRQVGRQRWKDYHWKHIHDWXUHVXJJHVWHGE\WKHEHIRUH mentioned algorithm. In addition, conventional electric generators and cogeneration systems will be selected optimally according to economic and environmental criteria and automated through microcontrollers. Cost reductions and pollution due to the integration of renewable energy sources and cogeneration are then calculated to enhance the decision to integrate such systems. Several existing applications and algorithms deal with the integration of the renewable energy and cogeneration systems in the power grid. In [1-3], software and algorithms dealing with the integration of energy systems in a power grid are detailed and explained. We present the most popular systems in this domain. AEOLIUS shows the influence of the integration of energy sources (wind and solar energy) on conventional power plants. In this tool the electric system was simulated and took into account all thermal generation technologies, as well as wind, photovoltaic and geothermal [4-6]. BALMOREL was simulating the electricity sector and part of the heat sector (district heating). It also high spotted the applied cogeneration systems [7]. CHP Screening Tool evaluates the economic capacity of three-generation systems (cooling, heating and electricity) for buildings [8]. COMPOSE provides a realistic incertitude assessment of the distribution of costs and benefits [9]. E 4CAST analyses the energy sector by presenting the production of energy, the energy market and the power consumption in a global comprehensive manner [10]. EMCAS probes the possible impacts of external events on the electricity sector in a power system [11]. EMINENT assesses the yearly performance and the potential impact of technologies at an early stage in a channel energy production [12]. EMPS simulates and optimizes the operation of energy systems with some hydropower capacity [13]. EnergyPLAN simulates the energy system including the production of heat and electricity as well as the transport sector and industry [14]. EnergyPro investigates merely a power plant or cogeneration and all types of thermal power [15]. ENPEPP-BALANCE relates the demand for energy to available resources and technologies. It uses a market-based simulation approach to determine the response [16]. GTMax simulates the electric generators dispatching and the economic energy market between public utility companies [17, 18]. H 2RES simulates the integration of renewable energy technologies in energy systems, especially for those installed on distant islands [19]. HOMER simulates and optimizes the autonomous power systems connected to the grid with any combination of renewable energy sources, for both electrical and thermal applications, for urban or individual heating systems, including all costs like pollution charges excepting those used on fuel [20]. HYDROGEMS analyses the performance of hydrogen energy systems and specifically simulates the hydrogen mass flow, power consumption and electricity production. It is also used in simulation of the thermal performance of integrated hydrogen systems [21]. iGRHYSO uses various technologies to simulate and optimize photovoltaic and wind energy, as well as small hydroelectric plants with storage batteries [22]. IKARUS simulates the production, storage / conversion, and transport technologies of the energy system. Its main purpose is to reduce the total cost of the system and emissions [23]. INFORSE is an energy balancing tool that deals with related worksheets used to enter the details of the modelled power system, including details of the production, demand and policies [24]. INVERT simulates thermal and renewable energy excepting nuclear, waves and tides. Evaluation of the effects of different promotions, all costs and economic exchanges are the main purpose of this tool [25]. LEAP is used to analyse national energy systems and to track energy consumption, production and resource extraction in all economy sectors [26]. MARKAL / TIMES works on the best energy system for each period of time, by selecting all options that minimize the cost and reduces the total surplus over the planning. This tool is flexible with any imposed political and physical constraints [27]. MESAP PlaNet analyses and simulates all energy variables of local, regional, national and global energy systems. A specific cost of production is determined by a detailed calculation of the cost of all products in the reference energy system [28]. MESSAGE works on planning of energy systems at medium to long terms by optimizing the engineering systems, analysing the climate change policies and measuring of the limitations and reduction of greenhouse gas emissions [29]. MiniCAM simulates the economy, energy consumption and emissions by focusing on energy production technologies, including the production of electricity, hydrogen, synthetic fuel and geological sequestration of carbon from fossil fuels [30]. NEMS is the representation of the annual behaviour of energy markets and their interactions with the US economy. It evenly distributes the quantity that producers are willing to provide to different energy prices, with respect to consumer

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demand [31]. ORCED meets the electricity demands by making a yearly dispatching of power plants. It relates the production to the demand on an hourly basis, assuming no transmission constraints [32]. PERSEUS simulates all thermal power plants, renewable energy and storage / conversion technologies. Electric vehicles can also be simulated strictly in the transport sector [4, 5]. PRIMES can simulate all thermal and renewable energy storage / conversion and transportation technologies which is organized by a subsystem of energy production supply consisting of oil, natural gas, coal, electricity and heat production, and biomass, except for battery energy storage, compressed air storage, and electric and smart hybrid vehicles [33]. ProdRisk models the electricity sector and simulates four technologies: thermal power, wind power, hydroelectric power and energy storage for hydroelectric pumping [34]. RAMSES considers the operation of existing facilities, as well as reinvestment in new plants on a yearly basis. The results are the primary energy consumption, integrated renewable energy, CO2 emissions. It also considers all the costs and heat generation technologies in a national energy system, as well as wind, hydro, photovoltaic and geothermal heat pumps, energyhydroelectric pumped storage, storage of compressed air energy, and energy storage battery [35]. RETScreen can be applied to any energy system, from individual to global applications. All thermal and renewable generation technologies can be considered. It can also easily integrate energy efficiency measures [36]. SimREN uses independent tool for energy demand, energy management tailored distribution systems and energy production. It is mainly used to simulate different energy systems based on renewable sources [37]. SIVAEL manages power plants, cogeneration plants and wind power, energy storage battery, and energy exchange with other countries. A stochastic process can be used to reproduce the real model by simulating wind forecast errors [38]. SOMES simulates electricity production of renewable energy generators on hourly basis. The model can optimize tasks to reach lower cost of electricity in the predefined constraints [39]. STREAM creates an overview of greenhouse gas emissions, energy resources, fuel consumption, and fuel conversion in the energy system by using the flow of energy tool [40]. TRNSYS16 separates the individual components of the entire energy system to simulate its performance, mainly used to analyse a single project, a local community or an island power system [41]. UniSyD3.0 Provides electricity production and hydrogen profiles, power generation and hydrogen prices, volumes of greenhouse gases, consumption of primary energy, cost of water and cost of atmospheric pollution [42]. WASP allows working on a plan for an energy production system over a long period according to defined constraints. Cost function that is composed of all the costs in the system is used to compare different solutions [43]. WILMAR Planning Tool analyses the evolution of operating costs in the electricity sector versus the rise of the integrated wind power and evaluates how boilers and electric heat pumps can improve the feasibility of large integrated wind energy. In other terms it simulates the integration of wind energy into the power system [44]. This paper will be divided as follows: In the second section, the principle of operation of the new algorithm will be explained. In the third section, we will apply the algorithm to a general case of electricity production and discuss the results. 2. Proposed algorithm 2.1. General description The developed algorithm is used to control the power output of conventional generators and cogeneration systems to reduce the production cost and pollution. It takes into account the electrical and thermal demands and instantaneous production of renewable energy and the operation of cogeneration systems integrated in the grid. Its period of operation is a default time and it can be changed by the operator. At the end of each period, the algorithm calculates the operating cost and emissions and their reductions due to the integration of ER and SC in the power grid. This algorithm can be considered as a benchmark decision making with the overall vision of the energy system presented and compared with that of the conventional case (i.e. without renewable energy and cogeneration systems). This novel algorithm, which is developed with MATLAB programming language, has not the same characteristics as those listed above. In this section, the principle of operation will be detailed to confirm its distinction with its rivals. 2.2. Algorithm inputs and outputs The input parameters to this algorithm are:

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Hourly based thermal demand; Hourly based electricity demand; Hourly based renewable Energy Production (Wind, Solar, and Hydro); Matrix representing conventional generators in ascending order of the average operating cost ¼0:K ; 0DWUL[UHSUHVHQWLQJWKHFRJHQHUDWLRQV\VWHPVLQDVFHQGLQJRUGHURIWKHDYHUDJHRSHUDWLQJFRVW¼0:K  The output parameters of this algorithm are: Generators matrix; CS matrix; Electricity generated by the cogeneration systems; Thermal energy produced by cogeneration systems; Electric energy produced by conventional generators; Total electric power supplied to the network; Total operating cost; Lower total cost due to the integration of RE and CS; Total exhaust emissions (CO2); Reduction of emissions due to the integration of RE and CS. 2.3. Operating steps The operation of the algorithm is performed in 7 steps (Fig.1): 1. Data acquisition: At each time interval (default is one hour), the algorithm repeats the same steps as in an electrical system since the conditions change continuously. In fact, the period of one hour is chosen because it is a fairly sufficient time during which the generator is operating normally and the input data (electricity demand, thermal demand, etc.) of the distribution network are provided. Then, the algorithm will reinitialize to acquire new data so that it can proceed to the second step. 2. Selection of cogeneration systems: After acquiring the heat demand, the algorithm calculates the best operating power needed for the cogeneration systems to cover the heat demand. In fact, cogeneration systems are preferred in the case of need of thermal energy; otherwise the heat generated by these systems will be useless. The algorithm also shows the excess of power if the thermal demand exceeds the maximum power that can be generated. The systems will be selected incrementally, knowing that they are ranked in ascending order of their average operating cost. The selection of systems is provided using the binary digits 0 and 1. The purposes of the calculation of power cogeneration in this step are: benefit from the heat produced by the CS, reduce production costs and find the remaining power to meet electricity demand. 3. Selection of conventional generators: After calculating the remaining power to meet electricity demand, the algorithm selects the traditional generators incrementally. These generators are also ranked in ascending order of their average operating cost. They are indeed chosen to produce the higher minimum power to the power required. If it exceeds the maximum power that could be produced, the algorithm shows that the power is exceeded. The selection is also translated by using the binary digits. 4. Calculation of the total cost of operation and current emissions: The total cost of operation and current emissions is calculated from the sum of the corresponding operation costs and emissions of the selected cogeneration systems and conventional generators respectively. 5. Calculation of operation costs and emissions in the traditional case: Operation costs and emissions in the traditional case are calculated taking into consideration that there is no ER or CS, and assuming that the thermal demand is an electric demand. Indeed, the thermal demand in this case is generally covered from electrical energy. As a result, the electrical demand in this context is equal to the sum of the electrical and thermal applications data. 6. Calculation of the total cost reduced and the reduced emissions due to the integration of RE and CS: The total cost reduced is calculated from the difference between the total operation cost in the traditional case and the total actual operation cost. The reduced emissions are regarded as the difference between the total emissions in the tradintional case and the total actual emissions. 7. Display of the outputs: After all the calculations being performed, the outputs of the algorithm are displayed in this final step. 3. Experiments and results The proposed algorithm is tested considering a metropolitan area of one million inhabitants with an average electricity demand of 1,100 MW. Generators and installed cogeneration systems are presented in the figures 2-5. Renewable energy (wind and solar) have an average installed capacity of 150 MW. They are considered ideal (zero emission). The thermal power produced by the cogeneration system is approximately equal to 5/3 of its electrical power. It is necessary in the case of this region to distinguish between thermal demand and electricity demand to benefit from the heat produced by the cogeneration systems. Simulations study four different cases: 1. Electric demand = 600 MW; Thermal demand = 400 MW; RE = 150 MW

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2. Electric demand = 850 MW; Thermal demand = 300 MW; RE = 150 MW 3. Electric demand = 1000 MW; Thermal demand = 200 MW; RE = 100 MW 4. Electric demand = 1000 MW; Thermal demand = 450 MW; RE = 0 MW Results of the four different cases are displayed in figures 2 through 5 (Fig. 2, 3, 4, 5). In these figures, the simulation results are displayed in the following order: - Inputs: Load (MW); Thermal Load (MW); Renewable Energy (MW). - Output Values: Cogeneration electric power (MW); Conventional electric power (MW); Thermal power (MW); Total electric power (MW). - Conventional generators matrix: >*HQHUDWRU¶V SRZHU 0:  *HQHUDWRU¶V VWDWH  LI RII DQG  LI RQ  Fuel FRVWKRXU¼ Actual fuel cost/hour ¼ 3olluting emissions/hour (kg); Actual Polluting emissions/hour (kg); *HQHUDWRU¶V VWDWH LI ZLWKRXW 5( DQG &6 (0 or 1); Fuel FRVWKRXU LI ZLWKRXW 5( DQG &6 ¼  3ROOXWLQJ emissions/hour if without RE and CS (kg)]. - Cogeneration systems matrix: [System¶V electric power (MW); System¶VVWDWH LIRIIDQGLIRQ  Fuel FRVWKRXU¼ Actual fuel cost/hour ¼ 3olluting emissions/hour (kg); Actual Polluting emissions/hour (kg)]. - Outputs: [Total fuel cost/hour (¼); Reduced cost/hour (¼); Polluting emissions/hour (kg); Reduced emissions/hour (kg)]. 4. Discussions From a general view of the results, we can see that the algorithm: - Monitors and controls the power generation and all traditional generators to take advantage of RE and CS. - Monitors and controls the thermal generation and all the CS to cover the heat demand and reduce the use of traditional generators. - Selects generators and CS in order to obtain electricity and heat production optimized with respect to the application. - Calculates the total cost reduction and emissions to highlight the economic and environmental impacts of RE and CS.

Fig.1. Algorithm flow chart

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From Table 1, we can deduce that: 1. Operation cost / MW decreases as the penetration level of CS and RE increases (Figure 6). 2. Polluting emissions / MW decrease when the penetration level of CS and RE increases (Figure 6). 3. Reduction of operation costs / MW is directly related to the penetration level of CS and RE (Figure 7). 4. Reduction of emissions / MW is directly related to the penetration level of CS and RE (Figure 7). According to the results, the environmental and economic impacts of CS and RE are positively highlighted. Thus, the proposed algorithm could be considered as an efficient decision making tool.

Fig.2. Results of study case 1

Fig.3. Results of study case 2

Fig.4. Results of study case 3

Fig.5. Results of study case 4

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Case 1

Case 2

Case 3

Case 4

CS penetration level (%)

42.19

19.35

16.8

25.4

RE penetration level (%)

23.44

16.13

9.34

0

CS+RE penetration level (%)

65.63

35.48

26.14

25.4

2SHUDWLRQFRVW0:¼0:

81.4

106.24

119.44

128.11

5HGXFHGFRVW0:¼0:

156.4

57.4

22.8

15.5

Polluting emissions /MW (kg/MW)

118.87

209.73

250.05

252.43

Reduced polluting emissions /MW (kg/MW)

457.7

187

94.8

95.7

Table 1. Comparison of case studies results

Fig.6. Economic and environmental impact of the CS and RE penetration level

Fig.7. Reduced cost and emissions due to CS and RE penetration level

5. Conclusion In this paper, the algorithm optimizing power production generators and conventional power plants is presented. Its role is to manage, control and simulate the power grid. In addition, it can be a very important tool for decision making in the installation of renewable energy and cogeneration systems. This algorithm is used to control the power output of conventional generators and cogeneration systems to reduce the production cost and pollution. It takes into account the electrical and thermal demands and instantaneous production of renewable energy and the operation of cogeneration systems integrated in the network. In addition, it calculates the operating cost and emissions and their reductions due to the integration of non-polluting systems (RE and CS). Acknowledgements The authors gratefully acknowledge the National Council for Scientific Research (CNRS) of Lebanon for the support given to Joseph AL ASMAR during his researches.

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