A new combined control algorithm for PV-CHP hybrid systems

Due to the 2012 change in the renewable energy act the feed-in tariffs, and therefore the number of newly installed photovoltaic systems decreased dramatically in Germany. Particularly in the residential sector as the biggest market new business ideas for photovoltaic systems were developed. Hence a photovoltaic and combined heat-and-power system, which provides not only electricity but also heat. This complex system requires flexible control strategies. A new combined control algorithm is proposed that in contrast to the standard strategies can operate even under incorrect weather and load forecasts without creating discomfort. Furthermore is it applicable for cloud-based solutions. In this paper a high-level model predictive control based on a mixed integer linear programming problem is combined with an additional low-level, rule-based controller. The low-level control compares the set-points of the optimization with the actual values and corrects the set-points according to each system component until the next optimization takes place. The results show that a hybrid system can be successfully controlled by a combined control approach. In case of a cloud-based application the need for an optimization can be reduced by a factor of four without diminishing comfort or facing much higher operational costs. It has also been shown that the combined control algorithm can be used as an energy management of microgrids.

[1]  Servando Álvarez Domínguez,et al.  Analysis of the economic feasibility and reduction of a building’s energy consumption and emissions when integrating hybrid solar thermal/PV/micro-CHP systems , 2016 .

[2]  N S Jayalakshmi,et al.  An Integrated Control and Management Approach of Stand-alone Hybrid Wind/PV/Battery Power Generation System with Maximum Power Extraction Capability* , 2017 .

[3]  Josep M. Guerrero,et al.  Computational optimization techniques applied to microgrids planning: A review , 2015 .

[4]  Martin Braun,et al.  PHOTOVOLTAIC SELF-CONSUMPTION IN GERMANY USING LITHIUM-ION STORAGE TO INCREASE SELF-CONSUMED PHOTOVOLTAIC ENERGY , 2009 .

[5]  P. Kriett,et al.  Optimal control of a residential microgrid , 2012 .

[6]  Sarat Kumar Sahoo,et al.  A review on state of art development of model predictive control for renewable energy applications , 2017 .

[7]  John Allison,et al.  Robust multi-objective control of hybrid renewable microgeneration systems with energy storage , 2017 .

[8]  K. Palanisamy,et al.  Optimization in microgrids with hybrid energy systems – A review , 2015 .

[9]  Guido Lorenzi,et al.  Comparing demand response and battery storage to optimize self-consumption in PV systems , 2016 .

[10]  Steffen Petersen,et al.  The effect of weather forecast uncertainty on a predictive control concept for building systems operation , 2014 .

[11]  Bart De Schutter,et al.  Demand Response With Micro-CHP Systems , 2011, Proceedings of the IEEE.

[12]  Alberto Bemporad,et al.  Optimal energy management of a small-size building via hybrid model predictive control , 2017 .

[13]  Saad Mekhilef,et al.  Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid , 2017 .

[14]  Kuo-Hao Chang,et al.  Optimal design of hybrid renewable energy systems using simulation optimization , 2015, Simul. Model. Pract. Theory.

[15]  Ian Beausoleil-Morrison,et al.  Micro-cogeneration versus conventional technologies: Considering model uncertainties in assessing the energy benefits , 2017 .

[16]  Ahmet Palazoglu,et al.  Operational optimization and demand response of hybrid renewable energy systems , 2015 .

[17]  Filip Johnsson,et al.  Solar photovoltaic-battery systems in Swedish households – Self-consumption and self-sufficiency , 2016 .

[18]  Hongbin Sun,et al.  Probabilistic power flow analysis considering the dependence between power and heat , 2017 .

[19]  Lazaros G. Papageorgiou,et al.  Optimal design of CHP-based microgrids: Multiobjective optimisation and life cycle assessment , 2015 .

[20]  Ferdinando Salata,et al.  Heading towards the nZEB through CHP+HP systems. A comparison between retrofit solutions able to increase the energy performance for the heating and domestic hot water production in residential buildings , 2017 .

[21]  Abdellatif Miraoui,et al.  Microgrid sizing with combined evolutionary algorithm and MILP unit commitment , 2017 .

[22]  Eric S. Fraga,et al.  An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study , 2015 .

[23]  Dirk Uwe Sauer,et al.  Optimization of self-consumption and techno-economic analysis of PV-battery systems in commercial applications , 2016 .

[24]  Adisa Azapagic,et al.  Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage , 2015 .

[25]  Jamshid Aghaei,et al.  Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems) , 2013 .

[26]  Massimiliano Renzi,et al.  Optimal sizing of hybrid solar micro-CHP systems for the household sector , 2015 .

[27]  Ahmet Palazoglu,et al.  An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation , 2014 .

[28]  David L. Woodruff,et al.  Pyomo: modeling and solving mathematical programs in Python , 2011, Math. Program. Comput..

[29]  Evangelos Rikos,et al.  Use of model predictive control for experimental microgrid optimization , 2014 .

[30]  A. Azapagic,et al.  Energy self-sufficiency, grid demand variability and consumer costs: Integrating solar PV, Stirling engine CHP and battery storage , 2015 .

[31]  M. H. Nehrir,et al.  Real-time energy management of an islanded microgrid using multi-objective Particle Swarm Optimization , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[32]  Evangelos Rikos,et al.  A Model Predictive Control Approach to Microgrid Operation Optimization , 2014, IEEE Transactions on Control Systems Technology.

[33]  Oriol Gomis-Bellmunt,et al.  Trends in Microgrid Control , 2014, IEEE Transactions on Smart Grid.

[34]  H. B. Gooi,et al.  Sizing of Energy Storage for Microgrids , 2012, IEEE Transactions on Smart Grid.