ΜΠΣ ΠΡΑΣΙΝΗ ΕΝΕΡΓΕΙΑ ΤΜΗΜΑ ΗΜ&ΤΥ ΜΠΣ ΠΡΑΣΙΝΗ ΕΝΕΡΓΕΙΑ ΤΜΗΜΑ ΗΜ&ΤΥ ΕΥΦΥΗΣ ΕΛΕΓΧΟΣ ΗΛΕΚΤΡΙΚΩΝ ΕΝΕΡΓΕΙΑΚΩΝ ΣΥΣΤΗΜΑΤΩΝ Καθηγητής Πέτρος Π. Γρουμπός Εργαστήριο Αυτοματισμού και Ρομποτικής Τμήμα ΗΜ&ΤΥ Πανεπιστήμιο Πατρών Α ΕΞΑΜΗΝΟ 2016-17

A NEW MODEL FOR RES POWERGENERATION USING FCMs Problem Definition Basic Theories of Fuzzy Cognitive Maps Renewable Energy System description and modeling

Layout Introduction Problem Definition Basic Theories of Fuzzy Cognitive Maps Renewable Energy System description and modeling Simulation Data Conclusions Future Research

Introduction Renewable energy sources are receiving much attention worldwide. Fossil fuels have low efficiency and high level of emissions. Renewable energy sources have sustainability, economic benefits and reduce chemical pollutants. The intermittent nature of many renewable resources makes hybrid necessary combinations of two or more power generation technologies, along with energy storage, to improve the system sustainability.

Problem Definition Construction and use of Fuzzy Cognitive Maps in modeling a renewable energy system in order to control the energy generation.

Basic Theories of Fuzzy Cognitive Maps Modeling a system as a collection of concepts and causal links between them. Nodes: Represent the system’s concepts. Concepts correspond to the characteristics of the system. Arrows: Interconnection between nodes. Show the cause-effect relationship between them. Interrelationships between two nodes (W): W>0 positive causality W<0 negative causality W=0 no relationship

Basic Theories of Fuzzy Cognitive Maps The value of each concept at every simulation step is calculated by applying the following calculation rule: Ait-1: the value of the concept Ci at the iteration step t-1 Ait : the value of the concept Ci at the iteration step t Wji : the weight of interconnection from concept Ci to concept Cj f : the sigmoid function

Renewable Energy System description and modeling The RES combines two renewable energy sources: solar and wind. Photovoltaic and wind-turbines collaborate with the batteries and the diesel generator to produce the necessary power to cover loads demand.

Renewable Energy System description and modeling Concepts C1: Sun Insolation C2: Environmental Temperature C3: Wind Velocity C4: Shadows C5: Wind Turbines C6: Photovoltaics C7: Diesel Generator C8: Batteries C9: Load C10: Delivered Power Output

Renewable Energy System description and modeling The initial Fuzzy Cognitive Map with the first values of concepts will be as follows:

Renewable Energy System description and modeling Five experts-energy consultants gave their opinion about the interaction between the concepts and informed us about how much the inputs influence the energy production. So, the weight matrix is presented below:

Simulation Data Four different cases are simulated, one for an autumn day, one for a winter day, one for a spring day and one for a summer day. The experts defined that the desired value of the Total Delivered Power must range inside the following region 0.77≤Desired Output Concept≤0.85 meaning 77% and 85% of the maximum daily energy consumption.

Simulation Data Four different cases are simulated, one for an autumn day, one for a winter day, one for a spring day and one for a summer day. The experts defined that the desired value of the Total Delivered Power must range inside the following region 0.77≤Desired Output Concept≤0.85 meaning 77% and 85% of the maximum daily energy consumption.

Simulation Data First Case: Autumn Day Initial Values of Inputs in an autumn day: C1: “MEDIUM” C2: “MEDIUM” C3: “MEDIUM” C4: “MEDIUM” C5: “MEDIUM” C6: “MEDIUM” C7: “MEDIUM” C8: “MEDIUM” C9: “HIGH”

Simulation Data First Case: Autumn Day Subsequent values till convergence in autumn: We extracted these results with the value of output concept to be C10=0.8022.

Simulation Data Second Case: Winter Day Initial Values of Inputs in an winter day: C1: “MEDIUM” C2: “LOW” C3: “HIGH” C4: “MEDIUM” C5: “MEDIUM” C6: “LOW” C7: “MEDIUM” C8: “MEDIUM” C9: “HIGH”

Simulation Data Second Case: Winter Day Subsequent values till convergence in winter: We extracted these results with the value of output concept to be C10=0.7913.

Simulation Data Third Case: Spring Day Initial Values of Inputs in an spring day: C1: “HIGH” C2: “MEDIUM” C3: “LOW” C4: “LOW” C5: “LOW” C6: “HIGH” C7: “LOW” C8: “MEDIUM” C9: “HIGH”

Simulation Data Third Case: Spring Day Subsequent values till convergence in spring: We extracted these results with the value of output concept to be C10=0.8066.

Simulation Data Fourth Case: Summer Day Initial Values of Inputs in an summer day: C1: “VERY HIGH” C2: “HIGH” C3: “VERY LOW” C4: “VERY LOW” C5: “LOW” C6: “HIGH” C7: “LOW” C8: “MEDIUM” C9: “HIGH”

Simulation Data Fourth Case: Summer Day Subsequent values till convergence in summer: We extracted these results with the value of output concept to be C10=0.8087.

Conclusions The results in all four cases showed that the outcome given by the model satisfied the experts’ predefined criteria. The system operates properly during the whole year and has an adaptive attribute that allows it to deliver the appropriate electric power, compared to the power demand.

Future Research The validation of the proposed model. Additional concepts in modeling of renewable energy systems especially for different geographical regions. Use more experts. Conduct simulation studies using real data for various applications. Use learning algorithms (Hebbian, Non-linear, etc.) to train the experts

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