Management of hybrid energy supply systems in buildings using mixed-integer model predictive control

2015 ◽  
Vol 98 ◽  
pp. 470-483 ◽  
Author(s):  
Barbara Mayer ◽  
Michaela Killian ◽  
Martin Kozek
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Energy Supply ◽  
Mixed Integer ◽  
Hybrid Energy ◽  
Supply Systems ◽  
Energy Supply Systems

Evaluation of Performance Robustness of a Gas Turbine Cogeneration Plant Based on a Mixed-Integer Linear Model

2016 ◽  
Author(s):  
Ryohei Yokoyama ◽  
Ryo Nakamura ◽  
Tetsuya Wakui ◽  
Yuji Shinano
Keyword(s):  
Linear Model ◽  
Energy Supply ◽  
Mixed Integer ◽  
Design Stage ◽  
Cogeneration Plant ◽  
Energy Demands ◽  
Integer Linear Model ◽  
Supply Systems ◽  
Energy Supply Systems ◽  
Performance Robustness

In designing energy supply systems, designers are requested to rationally determine equipment types, capacities, and numbers in consideration of equipment operational strategies corresponding to seasonal and hourly variations in energy demands. However, energy demands have some uncertainty at the design stage, and the energy demands which become certain at the operation stage may differ from those estimated at the design stage. Therefore, designers should consider that energy demands have some uncertainty, evaluate the performance robustness against the uncertainty, and design the systems to heighten the robustness. Especially, this issue is important for cogeneration plants, because their performances depend significantly on both heat and power demands. Although robust optimal design methods of energy supply systems under uncertain energy demands were developed, all of them are based on linear models for energy supply systems. However, it is still a hard challenge to develop a robust optimal design method even based on a mixed-integer linear model. At the first step for this challenge, in this paper, a method of evaluating the performance robustness of energy supply systems under uncertain energy demands is proposed based on a mixed-integer linear model. This problem is formulated as a bilevel mixed-integer linear programming one, and a sequential solution method is applied to solve it approximately by discretizing uncertain energy demands within their intervals. In addition, a hierarchical optimization method in consideration of the hierarchical relationship between design and operation variables is applied to solve large scale problems efficiently. Through a case study on a gas turbine cogeneration plant for district energy supply, the validity and effectiveness of the proposed method and features of the performance robustness of the plant are clarified.

A Multi-Stage Optimization Approach for Energy Supply Systems With Discrete Design Decisions

2019 ◽  
Author(s):  
S. Bruche ◽  
G. Tsatsaronis
Keyword(s):  
Energy Supply ◽  
Original Problem ◽  
Superior Performance ◽  
Mixed Integer ◽  
Optimization Models ◽  
Optimization Approach ◽  
Energy Supply System ◽  
Multi Stage ◽  
Supply Systems ◽  
Energy Supply Systems

Abstract Mixed integer linear programming is frequently applied to identify promising design solutions of energy supply systems. However, application-relevant optimization models are often associated with complicating model features, e.g. numerous discrete design candidates or a large time horizon of the optimization. So, even state-of-the-art solvers may be confronted with major challenges to find satisfying solutions within reasonable time. In this paper a systematic multi-stage optimization approach is proposed that is intended to support the available algorithms in solving these complex problems. The basic idea of the approach is the distribution of the original problem into two major levels. On the first level, promising design candidates are generated using simplified optimization models. These simplifications are achieved through time series aggregation and the relaxation of operational binary variables. In the second stage, the objective values of the design candidates for the original problem are determined. The division of the problem into two stages leads to a significant reduction in required optimization time but simultaneously leads to an uncertainty regarding the quality of the found solution. Therefore, in a subsequent step, it is checked whether the objective value is within an acceptable distance from the theoretically best solution. If this is not the case, the first two steps are iteratively repeated. The proposed multi-stage approach is applied to the optimization of an energy supply system located in Germany. The results show a superior performance regarding required optimization time over conventional methods.

scholarly journals Optimization of operation of energy supply systems with co-generation and absorption refrigeration

2012 ◽  
Vol 16 (suppl. 2) ◽  
pp. 409-422 ◽  
Author(s):  
Mirko Stojiljkovic ◽  
Bratislav Blagojevic ◽  
Goran Vuckovic ◽  
Marko Ignjatovic ◽  
Dejan Mitrovic
Keyword(s):  
Energy Supply ◽  
Electric Utility ◽  
Heat Pumps ◽  
Point Of View ◽  
Mixed Integer ◽  
System Control ◽  
Absorption Refrigeration ◽  
Simulation And Optimization ◽  
Supply Systems ◽  
Energy Supply Systems

Co-generation systems, together with absorption refrigeration and thermal storage, can result in substantial benefits from the economic, energy and environmental point of view. Optimization of operation of such systems is important as a component of the entire optimization process in pre-construction phases, but also for short-term energy production planning and system control. This paper proposes an approach for operational optimization of energy supply systems with small or medium scale co-generation, additional boilers and heat pumps, absorption and compression refrigeration, thermal energy storage and interconnection to the electric utility grid. In this case, the objective is to minimize annual costs related to the plant operation. The optimization problem is defined as mixed integer nonlinear and solved combining modern stochastic techniques: genetic algorithms and simulated annealing with linear programming using the object oriented ?ESO-MS? software solution for simulation and optimization of energy supply systems, developed as a part of this research. This approach is applied to optimize a hypothetical plant that might be used to supply a real residential settlement in Nis, Serbia. Results are compared to the ones obtained after transforming the problem to mixed 0-1 linear and applying the branch and bound method.

scholarly journals Comparative study of algorithms for optimized control of industrial energy supply systems

2020 ◽  
Vol 3 (S1) ◽  
Author(s):  
Thomas Kohne ◽  
Heiko Ranzau ◽  
Niklas Panten ◽  
Matthias Weigold
Keyword(s):  
Comparative Study ◽  
Industrial Production ◽  
Energy Supply ◽  
Simulation Models ◽  
Mixed Integer ◽  
Energy Prices ◽  
Manufacturing Companies ◽  
Industrial Energy ◽  
Supply Systems ◽  
Energy Supply Systems

Abstract Both rising and more volatile energy prices are strong incentives for manufacturing companies to become more energy-efficient and flexible. A promising approach is the intelligent control of Industrial Energy Supply Systems (IESS), which provide various energy services to industrial production facilities and machines. Due to the high complexity of such systems widespread conventional control approaches often lead to suboptimal operating behavior and limited flexibility. Rising digitization in industrial production sites offers the opportunity to implement new advanced control algorithms e. g. based on Mixed Integer Linear Programming (MILP) or Deep Reinforcement Learning (DRL) to optimize the operational strategies of IESS.This paper presents a comparative study of different controllers for optimized operation strategies. For this purpose, a framework is used that allows for a standardized comparison of rule-, model- and data-based controllers by connecting them to dynamic simulation models of IESS of varying complexity. The results indicate that controllers based on DRL and MILP have a huge potential to reduce energy-related cost of up to 50% for less complex and around 6% for more complex systems. In some cases however, both algorithms still show unfavorable operating behavior in terms of non-direct costs such as temperature and switching restrictions, depending on the complexity and general conditions of the systems.

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