Smart rules and thermal, electric and hydro storages for the optimum operation of a renewable energy system

Abstract Smart energy systems are meant as groups of energy conversion units that fulfill the requirements of several users according to “smart rules”. This paper considers an existing energy system including various units fed by renewable sources which serve thermal and electric users in a mountain resort. The goal is to find smart rules to operate the system by identifying the best operating alternative among a complete set deriving from connection to or isolation from the electric grid, inclusion or exclusion of storage systems and/or by-pass in the heat recovery system of the CHP units. To this end, detailed design and off-design models of each alternative are first built using field data or data supplied by manufacturers. The operation of each alternative and the capacities of the thermal and electric storages are then optimized to obtain the maximum profit. Results show that both electric and thermal storage systems must be included when the system works in isolation from the grid, but the profit is negative. Conversely, when the system is connected to the grid, the best operation strategy is achieved by including a thermal storage, while the inclusion of the electric storage is disadvantageous.

[1]  A. T. Young,et al.  Revised optical air mass tables and approximation formula. , 1989, Applied optics.

[2]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[3]  Narayana Prasad Padhy,et al.  Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms , 2001 .

[4]  Eric S. Fraga,et al.  System Design of Renewable Energy Generation and Storage Alternatives for Large Scale Continuous Processes , 2015 .

[5]  Richard E. Rosenthal,et al.  GAMS -- A User's Guide , 2004 .

[6]  Sharifah Rafidah Wan Alwi,et al.  An MILP model for cost-optimal planning of an on-grid hybrid power system for an eco-industrial park , 2016 .

[7]  Dragoljub Kosanovic,et al.  Operational planning of combined heat and power plants through genetic algorithms for mixed 0-1 nonlinear programming , 2015, Comput. Oper. Res..

[8]  Ryohei Yokoyama,et al.  A MILP decomposition approach to large scale optimization in structural design of energy supply systems , 2002 .

[9]  Brian Vad Mathiesen,et al.  From electricity smart grids to smart energy systems – A market operation based approach and understanding , 2012 .

[10]  Jihong Wang,et al.  Overview of current development in electrical energy storage technologies and the application potential in power system operation , 2015 .

[11]  Ryohei Yokoyama,et al.  Optimal structural design of residential cogeneration systems in consideration of their operating restrictions , 2014 .

[12]  Ruixian Cai,et al.  Typical off-design analytical performances of internal combustion engine cogeneration , 2009 .

[13]  F. Haghighat,et al.  Integration of storage and renewable energy into district heating systems: A review of modelling and optimization , 2016 .

[14]  Tetsuya Wakui,et al.  Optimization of energy supply systems by MILP branch and bound method in consideration of hierarchical relationship between design and operation , 2015 .

[15]  Ryohei Yokoyama,et al.  Optimal Operational Planning of Cogeneration Systems With Thermal Storage by the Decomposition Method , 1995 .

[16]  J. Duffie,et al.  Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation , 1982 .

[17]  Josie Close,et al.  Efficiency model for photovoltaic modules and demonstration of its application to energy yield estimation , 2007 .

[18]  Ryohei Yokoyama,et al.  Optimal structural design of residential power and heat supply devices in consideration of operational and capital recovery constraints , 2016 .

[19]  Per Heiselberg,et al.  Zero energy buildings and mismatch compensation factors , 2011 .

[20]  Cristina Elsido,et al.  Two-stage MINLP algorithm for the optimal synthesis and design of networks of CHP units , 2017 .

[21]  David Connolly,et al.  Smart energy and smart energy systems , 2017 .

[22]  Malcolm Irving,et al.  Large scale unit commitment using a hybrid genetic algorithm , 1997 .

[23]  Andrea Lazzaretto,et al.  Optimization of Thermal Power Plants Operation in the German De-Regulated Electricity Market Using Dynamic Programming , 2012 .

[24]  S. Rech,et al.  From Component to Macro Energy Systems: A Common Design and Off-Design Modeling Approach , 2011 .

[25]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[26]  Christoph Koch,et al.  The contribution of heat storage to the profitable operation of combined heat and power plants in liberalized electricity markets , 2012 .

[27]  Poul Alberg Østergaard,et al.  Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design , 2016 .

[28]  Joao P. S. Catalao,et al.  Energy storage systems supporting increased penetration of renewables in islanded systems , 2014 .

[29]  Ryohei Yokoyama,et al.  A mixed-integer linear programming approach for cogeneration-based residential energy supply networks with power and heat interchanges , 2014 .

[30]  Andrea Toffolo,et al.  TSO-STO: A two-step approach to the optimal operation of heat storage systems with variable temperature tanks , 2012 .

[31]  Masatoshi Sakawa,et al.  Operational planning of district heating and cooling plants through genetic algorithms for mixed 0-1 linear programming , 2002, Eur. J. Oper. Res..

[32]  Pierre Ineichen,et al.  Solar radiation transposition models applied to a plane tracking the sun , 1988 .

[33]  Henrik Lund,et al.  Renewable Energy Systems: A Smart Energy Systems Approach to the Choice and Modeling of 100% Renewable Solutions , 2014 .

[34]  Augusto Q. Novais,et al.  Symmetry breaking in MILP formulations for Unit Commitment problems , 2016, Comput. Chem. Eng..

[35]  Ryohei Yokoyama,et al.  Development of a General-Purpose Optimal Operational Planning System for Energy Supply Plants , 1994 .

[36]  Ryohei Yokoyama,et al.  A Revised Decomposition Method for MILP Problems and Its Application to Operational Planning of Thermal Storage Systems , 1996 .