Combined Heat and Power Units Sizing and Energy Cost Optimization of a Residential Building by Using an Artificial Bee Colony Algorithm

Battery manufacturing and recycling are expensive; combined heat and power (CHP) units are optimal for residential premises. CHP units can enhance energy efficiency and reduce energy costs, but appropriately sized units must be chosen. Here, we optimize CHP unit sizing to minimize the energy costs of residential areas. Sizing is based on both the electricity and heat loads; it is possible to optimally rate the various types of CHP units. We compare an artificial bee colony (ABC) optimization method to a genetic algorithm (GA) when various strategies are adopted. Electricity and heat loads are considered together when sizing CHP units and optimizing costs using the ABC algorithm and the GA. The optimization outcomes are compared to a base case; the ABC method performs better than the GA. The average daily energy cost savings possible using the ABC method were higher for all three seasons (by 25.9, 4.4, and 10.8% respectively) compared to those possible when residential premises lacked CHP units.

[1]  Ziad M. Ali,et al.  A Modified Balancing Approach for Renewable Based Microgrids Using Deep Adversarial Learning , 2020 .

[2]  K. Steemers,et al.  A method of formulating energy load profile for domestic buildings in the UK , 2005 .

[3]  Ryozo Ooka,et al.  Building energy system optimizations with utilization of waste heat from cogenerations by means of genetic algorithm , 2010 .

[4]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[5]  H. Aki.  The Penetration of Micro CHP in Residential Dwellings in Japan , 2007, 2007 IEEE Power Engineering Society General Meeting.

[6]  Fariborz Haghighat,et al.  Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power , 2019, Renewable Energy.

[7]  T. M. Kneiske,et al.  Testing a model predictive control algorithm for a PV-CHP hybrid system on a laboratory test-bench , 2019 .

[8]  Ali Mohammad Ranjbar,et al.  Financial analysis and optimal size and operation for a multicarrier energy system , 2012 .

[9]  Nikos D. Hatziargyriou,et al.  Dynamic Structural Sizing of Residential Energy Hubs , 2020, IEEE Transactions on Sustainable Energy.

[10]  R. Hanitsch,et al.  Technical and economical comparison of micro CHP systems , 2005, 2005 International Conference on Future Power Systems.

[11]  Michele Pinelli,et al.  Optimal design of a hybrid energy plant by accounting for the cumulative energy demand , 2019 .

[12]  Yaodong Wang,et al.  A domestic CHP system with hybrid electrical energy storage , 2012 .

[13]  Hongbin Sun,et al.  Active Demand Response Using Shared Energy Storage for Household Energy Management , 2013, IEEE Transactions on Smart Grid.

[14]  M. Aliehyaei.  OPTIMIZATION OF MICRO GAS TURBINE BY ECONOMIC, EXERGY AND ENVIRONMENT ANALYSIS USING GENETIC, BEE COLONY AND SEARCHING ALGORITHMS , 2020 .

[15]  Graham Coates,et al.  Sizing of residential CHP systems , 2011 .

[16]  Hongbo Ren,et al.  Economic and environmental evaluation of micro CHP systems with different operating modes for residential buildings in Japan , 2010 .

[17]  Dezhi Li,et al.  Sizing Combined Heat and Power Units and Domestic Building Energy Cost Optimisation , 2017 .

[18]  Barbara Praetorius,et al.  MICRO COGENERATION: TOWARDS A DECENTRALIZED AND SUSTAINABLE GERMAN ENERGY SYSTEM? , 2006 .

[19]  Behnam Mohammadi-Ivatloo,et al.  Optimal robust operation of combined heat and power systems with demand response programs , 2019, Applied Thermal Engineering.

[20]  Michele Pinelli,et al.  Dynamic programming based methodology for the optimization of the sizing and operation of hybrid energy plants , 2019, Applied Thermal Engineering.

[21]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[22]  Wencong Su,et al.  Multi-agent energy management of smart islands using primal-dual method of multipliers , 2020 .

[23]  J. Driesen,et al.  Multiobjective Battery Storage to Improve PV Integration in Residential Distribution Grids , 2013, IEEE Transactions on Sustainable Energy.

[24]  H. Moayedi,et al.  Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings , 2020 .

[25]  David Infield,et al.  Domestic electricity use: A high-resolution energy demand model , 2010 .

[26]  Whei-Min Lin,et al.  Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization , 2015 .

[27]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[28]  Hassan Ghasemi,et al.  Residential Microgrid Scheduling Based on Smart Meters Data and Temperature Dependent Thermal Load Modeling , 2014, IEEE Transactions on Smart Grid.

[29]  A. Zein El-Din,et al.  Multi-objective genetic algorithm to improve the power system operation using distributed generation , 2006, 2006 Eleventh International Middle East Power Systems Conference.