Solving district heating optimization problems in the market conditions

2016 ◽  
Author(s):  
Igor Moshkin ◽  
Antans Sauhats
Keyword(s):  
Optimization Problems ◽  
District Heating ◽  
Market Conditions ◽  
Heating Optimization

scholarly journals Optimization of district heating production with thermal storage using mixed-integer nonlinear programming with a new initialization approach

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Jakob Bjørnskov ◽  
Lasse Kappel Mortensen ◽  
Konstantin Filonenko ◽  
Hamid Reza Shaker ◽  
Muhyiddine Jradi ◽  
...  
Keyword(s):  
Convex Optimization ◽  
Production Planning ◽  
Energy Production ◽  
Optimization Problems ◽  
Time Window ◽  
District Heating ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
District Heating System ◽  
Complex Models

AbstractNon-convex scheduling of energy production allows for more complex models that better describe the physical nature of the energy production system. Solutions to non-convex optimization problems can only be guaranteed to be local optima. For this reason, there is a need for methodologies that consistently provide low-cost solutions to the non-convex optimal scheduling problem. In this study, a novel Monte Carlo Tree Search initialization method for branch and bound solvers is proposed for the production planning of a combined heat and power unit with thermal heat storage in a district heating system. The optimization problem is formulated as a non-convex mixed-integer program, which is incorporated in a sliding time window framework. Here, the proposed initialization method offers lower-cost production planning compared to random initialization for larger time windows. For the test case, the proposed method lowers the yearly operational cost by more than 2,000,000 DKK per year. The method is one step in the direction of more reliable non-convex optimization that allows for more complex models of energy systems.

CONCEPTUAL POSITIONS OF MODERNIZATION OF EXISTING INEFFICIENT DISTRICT HEATING SYSTEMS

2017 ◽  
pp. 11-21
Author(s):  
Ye.Ye. Nikitin
Keyword(s):  
Energy Efficient ◽  
Mutual Influence ◽  
District Heating ◽  
Current Status ◽  
Cognitive Approach ◽  
Heating Systems ◽  
Energy Management Systems ◽  
Current State ◽  
Self Sufficiency ◽  
District Heating Systems

The current situation in the sphere of district heating is analysed on the basis of use of the cognitive approach. The presence of closed chains of cause-effect relationships of negative factors and conflicts of target settings of the subjects in the field of district heating is shown. The conceptual model of energy efficient modernization of district heating systems is proposed. This model includes indicators of the current status of heat sources, networks and heat consumers, energetic and economic models, restrictions, procedure of forming and analysis of the mutual influence of the recommended projects. The quantitative data on indicators of the current state of district heating systems of the cities of Ukraine are presented. The interrelation between indicators of the current state and projects of energy efficient modernization of district heating systems is shown. Assessment of energy self-sufficiency of municipal district heating systems on condition of thermal modernization of buildings is carried out. The creation of energy management systems at the district heating enterprises is proposed. Bib. 6, Fig. 7, Tab. 5.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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