Fuzzy Logic

Abstract Energy demand management or Demand Side Management (DSM) involves actions that influence the pattern of energy consumption by consumers. In this paper a fuzzy logic based approach towards shifting the average power demand of residential electric water heaters has been discussed. Power system demand side management programs are strategies designed to alter the shape of the load curve. This paper targets both customer satisfaction and utility unit commitment savings, based on a fuzzy load model for the direct load control of appliances.

Problem Definition Load management is the process of balancing the supply of electricity on the network with the electrical load by adjusting or controlling the load rather than the power station output. For example, the cost of electricity is highest when the air conditioning load is greatest during hot afternoons. Load management programs or DSM programs are programs that intentionally alter the load shape of the customer by deliberate utility (an organisation that maintains the infrastructure for public service) intervention.

It is seen that in a typical city, the power consumed is maximum over the 8:00 am to 5:00 pm duration. With the ever increasing demand of electricity, even electric utility companies is faced with overwhelming demand peaks associated with a large amount of power being consumed at the same time. So, electric utility companies come up with price incentives for customers who participate in load management programs. Otherwise, these companies introduce a real time pricing strategy by which customers pay more for the electric power they use during high demand periods and less during low demand periods.

Some statistics collected at a typical residential area showed that the electric water heater was the single largest contributor towards total power consumption. This paper presents a fuzzy logic-based variable power control strategy, where the power consumed by the water heater can be controlled. The proposed fuzzy controller would shift the average residential water heater demand such that its peak demand periods occur when the total utility power demand is low and vice – versa.

Figure 1: Average residential daily total demand and water heater demand 1 Details about the Problem The electric water heater is an appliance which is used daily in the residential area taken up for study. Figure 1 clearly indicates that water heaters are a major contributor in the total power consumed. Thus, the electric water heater was chosen for customer or utility demand-side management (DSM) to shift part of the utility power demand from peak demand periods to off-peak periods.

The most common load management program is end-use equipment control, which is also known as direct load control (DLC). Existing electric water heater DSM strategies focus on: off control of the water heater, where a group of heaters are disabled or switched off during certain periods of time using this direct load control strategy. When water heaters are energized, they are either on, consuming a fixed amount of power, i. e. 4. 5 kW, or they are off.

A fuzzy logic-based variable power control strategy has been discussed where the power consumed by the water heater can be controlled based on the information available from the water heater such as water temperature, maximum and minimum water temperatures allowed (or desired) and distribution level (electric utility) power demand. Based on the status of the above variables, the fuzzy controller will determine the percentage of the maximum allowable power that the water heater should consume.

The fuzzy controller, which can be loaded on a microprocessor chip and installed on the water heater, can be tuned either by the customer or directly by the utility for those customers who participate in such DSM strategy. Based on this information, a control signal is generated to control the voltage applied to the water heater. Fuzzy Logic based Electric Water Heater Controller We know that = ( )2 . (1) Since the appliance under study is a heater, the resistive heating elements embedded in the appliance will consume the maximum percentage of power supplied.

We see from equation 1 that power consumed is proportional to the square of voltage supplied to the appliance. So, controlling the voltage supplied would ensure controlling the power consumed by the appliance. The block diagram for the proposed variable electric/ power water heater is shown in figure 2. Fuzzy logic control is a simple control strategy which works well for control of certain nonlinear systems that contain variables with uncertainty.

This control strategy can be applied in control of electric water heaters, which exhibit non-linearity between the power consumed by the water heater and the water temperature as well as exhibit uncertainty in the hot water usage and temperature profile. Figure 2 shows the block diagram for the proposed fuzzy controller. Max. Hot water temp. Min. hot water temp. AC power source Variable voltage/ power supplied to the heater Fuzzy Controller Electronic voltage controller Voltage control signal Electric Water Heater Hot water temp. Distribution level power demand

Figure 2: Block diagram for the fuzzy logic controller 2 The signal controlling the magnitude of the voltage applied is a function of four inputs. The fuzzy controller takes four crisp input values, fuzzifies them, gives a fuzzified control signal as output. They four inputs have been listed below along with their membership function plots. a) Demand This variable represents the average residential electric water heater power demand. They are categorised as low, average (low and high) and high. b) Temperature of Water This variable is a measure of the temperature of the water at any given time.

Temperature could be measured using a temperature sensor and is categorised as cold, warm (low, medium and high) and hot. 3 c) Comfort Level Comfort level is the minimum temperature for hot water set by the customer. The temperature of water is not to fall below this value. This temperature has been set at 95F in this study. d) Maximum Temperature The maximum water temperature set by the user. The temperature is set about 130F in this study. Output Variable: a) Power The percent power to be supplied to the unit as a fuzzy function. It is then defuzzified using the centroid technique.