A MILP model for integrated plan and evaluation of distributed energy systems

In the last decade, technological innovations and a changing economic and regulatory environment have resulted in a renewed interest for distributed energy resources (DER). However, because of the lack of a suitable design tool, the expected potential of DER penetration is not always exerted sufficiently. In this paper, a mixed-integer linear programming (MILP) model has been developed for the integrated plan and evaluation of DER systems. Given the site's energy loads, local climate data, utility tariff structure, and information (both technical and financial) on candidate DER technologies, the model minimizes overall energy cost for a test year by selecting the units to install and determining their operating schedules. Furthermore, the economic, energetic and environmental effects of the DER system can be evaluated. As an illustrative example, an investigation has been conducted of economically optimal DER system for an eco-campus in Kitakyushu, Japan. The result illustrates that gas engine is currently the most popular DER technology from the economic point of view. Although holding reasonable economic merits, unless combined with heat recovery units, the introduction of DER technologies may result in marginal or even adverse environmental effects. Furthermore, according to the results of sensitivity analysis, the optimal system combination and corresponding economic and environmental performances are more or less sensitive to the scale of energy demand, energy prices (both electricity and city gas), as well as carbon tax rate.

[1]  Adam Hawkes,et al.  Modelling high level system design and unit commitment for a microgrid , 2009 .

[2]  Frank Pettersson,et al.  Structural and operational optimisation of distributed energy systems , 2006 .

[3]  Satoshi Yoshida,et al.  Study on sustainable redevelopment of a densely built-up area in Tokyo by introducing a distributed local energy supply system , 2008 .

[4]  Taher Niknam,et al.  A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators , 2011 .

[5]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[6]  Xu Rong,et al.  A review on distributed energy resources and MicroGrid , 2008 .

[7]  Aqeel Ahmed Bazmi,et al.  Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review , 2011 .

[8]  Chris Marnay,et al.  Distributed energy resources customer adoption modeling with combined heat and power applications , 2003 .

[9]  Hongwei Li,et al.  Thermal-economic optimization of a distributed multi-generation energy system¿A case study of Beijing , 2006 .

[10]  Jinyue Yan,et al.  Potential and cost-effectiveness of CO2 reductions through energy measures in Swedish pulp and paper mills , 2003 .

[11]  Weijun Gao,et al.  Optimal option of distributed generation technologies for various commercial buildings , 2009 .

[12]  Marc Medrano,et al.  Integration of distributed generation systems into generic types of commercial buildings in California , 2008 .

[13]  Kari Alanne,et al.  Distributed energy generation and sustainable development , 2006 .

[14]  Hongbo Ren,et al.  Promotion of energy conservation in developing countries through the combination of ESCO and CDM: A case study of introducing distributed energy resources into Chinese urban areas , 2011 .

[15]  Giuseppe Forte,et al.  Environmental-constrained energy planning using energy-efficiency and distributed-generation facilities , 2008 .

[16]  Hendrik Neumann,et al.  Optimal operation of dispersed generation under uncertainty using mathematical programming , 2006 .

[17]  Sanya Carley,et al.  Distributed generation: An empirical analysis of primary motivators , 2009 .

[18]  Pierluigi Mancarella,et al.  Global and local emission impact assessment of distributed cogeneration systems with partial-load models , 2009 .

[19]  Pedro J. Mago,et al.  Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme , 2009 .

[20]  Jinyue Yan,et al.  Increasing biomass utilisation in energy systems: a comparative study of CO2 reduction and cost for different bioenergy processing options. , 2004 .

[21]  Zhiqiang Zhai,et al.  Performance comparison of combined cooling heating and power system in different operation modes , 2011 .

[22]  Antonella Meneghetti,et al.  Optimisation models for decision support in the development of biomass-based industrial district-heating networks in Italy , 2005 .

[23]  Rahul B. Hiremath,et al.  Decentralized energy planning; modeling and application—a review , 2007 .

[24]  Chris Marnay,et al.  An Analysis of the DER Adoption Climate in Japan Using Optimization Results for Prototype Buildings with U.S. Comparisons , 2006 .

[25]  Guohe Huang,et al.  Integer programming with random-boundary intervals for planning municipal power systems , 2010 .

[26]  Chris Marnay,et al.  Distributed Energy Resources Market Diffusion Model , 2006 .

[27]  Stein-Erik Fleten,et al.  Optimal Investment Strategies in Decentralized Renewable Power Generation Under Uncertainty , 2006 .