Integrated Energy Management System (EMS) Using Fuzzy Logic Control for Hybrid Power Network

Objective

The aim of this document is to outline a step-by-step approach to implementing an Integrated Energy Management System (EMS) that leverages fuzzy logic control. This EMS will manage energy within a hybrid power network, comprising photovoltaic (PV), wind, battery storage, and a utility grid, to optimize the use of renewable resources, maximize resilience, and ensure effective energy distribution

1. System Overview

1. System Components

   • Energy Sources: Hybrid setup including PV panels, wind turbines, and battery storage, along with a connection to the utility grid.

   • Load Types: Categorized by priority based on their energy consumption patterns (e.g., critical vs. non-critical loads).

   • Energy Management System (EMS): Central system implementing fuzzy logic control to optimize energy use and manage load distribution based on real-time demand and resource availability.

2. Key Goals

   • Optimize renewable energy use by balancing power from PV, wind, and the grid.

   • Minimize peak load consumption costs by managing load distribution.

   • Ensure system resilience while maintaining a satisfactory level of customer convenience.

2. Fuzzy Logic Control System Design

The fuzzy logic control system is central to the EMS and uses linguistic variables to model uncertain and dynamic aspects of energy management. This approach is divided into three stages: Fuzzification, Inference Engine with Membership Functions, and Defuzzification.

3. Step-by-Step Approach

   Step 1: Define Inputs and Outputs for Fuzzy Logic System

   • Inputs:

     • Power Demand: Real-time energy demand from the load.

     • Renewable Power Availability: Combined output from PV and wind sources.

     • State of Charge (SoC): Battery storage level, crucial for determining when to draw from or recharge the battery.

     • Electricity Cost: Real-time grid electricity prices.

   • Outputs:

     • Source Selection: Decision on how much power to draw from PV, wind, battery, or grid.

     • Load Adjustment: Determines load shedding or deferment actions for non-critical loads during peak demand.

   Step 2: Fuzzification

   Convert each input variable into fuzzy sets using membership functions to represent degrees of truth. Fuzzy sets translate each input into categories that are easy to interpret for decision-making.

   • Power Demand: Low, Medium, High

   • Renewable Power Availability: Low, Medium, High

   • SoC: Low, Medium, High

   • Electricity Cost: Low, Medium, High

   Each category will be represented by membership functions that define the degree to which a particular input belongs to each fuzzy set.

   Step 3: Define Membership Functions

   Define membership functions for each input variable based on the system’s operating range. Common membership functions include triangular and trapezoidal functions, which offer smooth transition boundaries and simplicity.

   For example:

   • Power Demand: Defined over a range [0, Max Demand], with triangular membership functions for "Low," "Medium," and "High."

   • Renewable Power Availability: Defined over a range based on potential renewable energy production.

   • SoC: Ranges from 0% (empty) to 100% (fully charged).

   • Electricity Cost: Based on utility pricing tiers, ranging from "Low" (off-peak) to "High" (peak).

   Step 4: Define Fuzzy Rules (Inference Engine)

   Fuzzy rules form the decision-making logic by combining input fuzzy sets to determine output actions. These rules incorporate expert knowledge about the energy system's operation.

   Examples of fuzzy rules:

   1. If Power Demand is High and Renewable Power Availability is High, and SoC is Medium, then Use Renewables and Defer Non-Critical Load.

   2. If Power Demand is Medium, Electricity Cost is High, and SoC is High, then Use Battery Storage.

   3. If Power Demand is Low and Electricity Cost is Low, then Use Grid Power and Recharge Battery.

   These rules determine how the system prioritizes different sources based on current operating conditions.

   Step 5: Defuzzification

   The fuzzy output from the inference stage needs to be converted into a specific actionable value. Centroid or weighted average methods are common defuzzification techniques, as they provide a single output value.

   For example:

   • For Source Selection, defuzzification provides the exact proportion of energy to draw from each source (renewables, battery, or grid).

   • For Load Adjustment, defuzzification provides the extent to which non-critical loads should be reduced or deferred.

   Step 6: Implement Control Logic

   Translate defuzzified values into EMS control actions:

   1. Allocate Power Sources: Distribute power among the renewable sources, battery storage, and grid based on fuzzy control decisions.

   2. Load Shedding and Deferment: During peak demand, reduce non-critical loads as directed by the fuzzy logic control.

   3. Battery Management: Charge or discharge the battery based on grid cost, SoC, and available renewable power.

   Step 7: Testing and Optimization

   • Simulate Scenarios: Test the fuzzy EMS under different load and renewable power conditions.

   • Tune Membership Functions and Rules: Adjust based on performance to improve control precision and customer convenience.

   • Real-Time Monitoring: Monitor system behavior to refine fuzzy rules and membership functions continuously.

4. Conclusion

The EMS using fuzzy logic control will ensure the optimal use of renewable resources and manage peak demand without over-reliance on the grid. This system’s intelligent control will minimize operational costs while enhancing grid resilience and maintaining acceptable customer convenience levels.