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The concept of Demand Side Management (DSM) was originated in the 1970’s in response to the impacts of energy shocks to the electricity utility industry. Energy problems truly became raised after the OPEC countries stopped their oil supply to countries that supported Israel during the Arab-Israeli War in October 1973. Arab supporters within OPEC countries stopped shipping oil to western supporters of Israel to punish them. The oil manufacturing countries lifted the ban in early 1974 but they raised the price of oil dramatically. Due to higher energy prices consumers are forced to spend more money on energy and less on other goods, as a result business activity slowed down. In order to reduce the electricity demand, the utilities began providing incentives to customers who curtailed their electricity use and thus the Demand Side Management came into existence.
Electricity demand is rapidly increasing with the increase in new infrastructures, new businesses and homes, and a huge rise in the use of air conditioners. This puts increasing pressure on the energy supplying utilities and forced them for large investments to cope with short peaks in demand. Demand management mainly aims to encourage a change in the use of electricity.
Residential load is the largest contributor for the increase in the peak demand. The importance of the residential consumers in the national demand of electrical energy and power has increased rapidly. The increase in the number of residential consumers of electrical power has been greater than the growth of the population as a whole. The residential load is responsive to the weather factors in the country, the life style of the people and the types and number of the appliances that they are using. The Demand Side Management related to residential consumers allows the consumers to control their loads and to know the behavior of their main electric appliances in relation to the demand of electrical energy. By various DSM techniques, the consumers can know their energy consumption profile and manage their loads accordingly.
During the peak period, most generator units generate close to the capacity limit, the electric system becomes stressed and the reliability of the whole system is damaged. Thus the system becomes more vulnerable to failure and the probability for the loss of load increases. The peak demand can be reduced by direct control of residential loads, but they were not much considered because of lack of proper control equipment. It is important to formulate energy conservation policies and to develop various DSM techniques, aiming specifically for residential consumers, seeking for load curve modulation and rational use of energy.
Demand Side Management covers a whole range of technology and policy measures designed to reduce electricity consumption from economic activities. DSM for microgrids has been researched considerably in the past years. The advancement of renewable energy technologies, particularly wind and solar systems coupled with the microgrid technology, offers important opportunities for remote communities in the country to stabilize long-term costs and improve local energy security. Demand side management plays a vital role in providing greater connection of intermittent renewable generation of electricity. DSM of renewable based microgrid also has some social benefits such as protection of global environment, reduction of environmental degradation, conservation of resources etc.
There are certain definitions for DSM. The most widely accepted one is the following. Demand-Side Management is defined as the planning and execution of those utility activities designed to influence the customer’s use of electricity in certain ways that will produce desired changes in the load shape of utility’s, i.e., in the magnitude and the time pattern of a utility’s load. In DSM various programs were implemented by utility companies in order to control the energy consumption at the customer side. Already available energy can be more efficiently utilized without installing new electricity generation and transmission infrastructure with the implementation of DSM programs. The demand response can decrease the need for building new power plants for the peak power periods. The traditional DSM activities taken by the utility company to alter the load shape can be characterized into six categories based on the state of the existing utility system. The figure below shows the load-shapes.
Peak clipping is a basic form of load management which helps in the reduction of system peak by using direct load control. While Valley filling is another type of load management which off-peak loads. This strategy may be desirable when the long-term average price is lower than the cost of load building in the off-peak hours. Load shifting is also one of the classic forms of load management taken by the utilities which involves shifting loads from peak to off-peak periods. Conventional devices for load shifting include space heating storage, cooling storage, domestic hot water storage, and customer load shifting, etc.
Strategic conservation is the load shape change that occurs from various targeted conservation activities. This strategy is not traditionally considered by the utilities as a load management option as it involves a reduction in sales not necessarily accompanied with peak reduction. Examples of strategic conservation efforts are building energy conservation and appliances efficiency improvement. Strategic load growth refers to a general increase in electricity sales beyond valley filling and the spontaneous effects of economic growth. Examples of strategic load growth include electrification, substitution for primary fuels, commercial and industrial process heating and automation and other means for increase in energy intensity in industrial and commercial sectors. Flexible load shape involves allowing customers to purchase some power at lower than normal reliability. The customer’s load shape will be flexible, depending on the real-time reliability conditions.
The Peak Clipping, Strategic Conservation and Valley Filling strategies are traditional load management approaches used by the utilities to alter the load shapes. The utilities provide incentives to target customers for more specific load shape changes to avoid construction of new generation units of relatively low usage at the time of high system loads. Whereas Strategic load Growth, Load Shifting and Flexible Load Shape strategies offer more systematic and large scale changes than the first three and the goal is not only to alter the peak valley structure, but also to change the ways in which electricity is used.
Residential load management programs usually aim at reducing consumption and shifting consumption. One approach in residential load management is direct load control (DLC). In DLC programs, based on a mutual agreement between the utility company and the users, the utility can control the operations and energy consumption of selected appliances in a household remotely. For example, it can control lighting, thermal comfort equipments, pumps and refrigerators and thus considerable energy as well as cost savings can be achieved. However, user’s privacy will be a major issue and becomes a barrier in implementing DLC programs, when it comes to the case of residential load control and home automation.
An alternative for Direct Load Control is smart pricing, where customers are encouraged to individually and voluntarily manage their loads, for example by reducing their consumption at peak hours. In this case various pricing strategies like Real Time Pricing (RTP) policy, Time of Use Pricing (ToUP) policy, Critical Peak Pricing (CPP) policy etc. are some among the popular options. For example, in Real Time Pricing tariffs, the price of electricity varies at different hours of the day and ornately designed pricing rules encourage the customers to individually and voluntarily manage their loads in order to reduce their own energy cost. According to RTP the electricity prices are usually higher during the afternoon, on hot days in summer, and on cold days in the winter. But it is usually making some difficulties for the users to manually respond to prices that are keeps changing every hour. Another important problem that RTP policy may face is the load synchronization, where a significant portion of the peak load is shifted from a typical peak hour to a typical nonpeak hour, but not reducing the peak to average ratio. Certain methods for residential load management are given below.
Here the Programmable Logic Controller (PLC) is integrated with an electrical energy meter, sensors and specific loads such as light bulbs, electric showers and air conditioning, aiming for a more efficient utilization of these end use appliances in a household. Supervisory software was implemented in a PC for managing the action of the controller on the loads. This software allows the communication in real time, through RS-232 which is a computer serial interface, between the PLC and the PC. The controller can turn the load on and off, in order to attain the aimed power and energy target. By this method, the energy consumption in a month for a household can be reduced by about 25% by the combined action of sensors and load control using power and energy targets. Even though the results of this controller are very promising, the comfort of consumer is affected here. Also the option to incorporate energy from renewable sources is not provided here.
In this method, DSM for a grid connected household with photovoltaic energy which is generated locally, is ensured through smart scheduling of the electrical appliances. At first the appliances time of use probabilities have been studied as a technique for learning the customer’s electricity demand pattern. After that these past power consumption behaviors of the particular customer is converted to device schedules which is then used to autonomously regulate the energy use. The tactic used in this method is to buy as little energy as possible from the grid and to export as much energy as possible to the grid without compromising its load energy requirements. Here manual participation of customers in demand response is not possible. The success of this method therefore lies in its full automation. Thus it has the advantage that the users who are not having the knowledge to appropriately respond to grid signals can also participate in this program.
Through various DSM methods, savings can be maximized with the utilization of renewable energy sources along with the optimized appliance selection. The DSM of a household by autonomous appliance scheduling is evaluated using the data obtained from 22 household appliances, where the customer has installed ten 1- KW (peak) PV arrays which represents about 75% of the total energy requirement. The results show that the utilization of renewable energy sources brought about 78% savings in energy cost when compared to the case where renewable energy is not utilized. Also the optimization of resource management and appliance selection shows a 20% increase in savings than without optimization. Feed In Tariff(FIT) policy is usually used in cases where renewable energy is involved. In FIT plan, the utility company buys all the PV generated electricity from the customer who in turn relies on the main grid to supply all his/her power requirement. In order to encourage consumers to join this scheme, the contractual energy price for purchasing power from the grid is set lower than the price at which the household sells PV energy to the grid. However, additional penalty charge is imposed on the consumer for submitting inaccurate PV energy forecasts to the utility company. The customer must therefore ensure that he/she provides very accurate power forecasts to the utility company.
Unscheduled load energy cost under FIT plan
Scheduled load energy cost under FIT policy
From the above two graphs it is clear that the maximum cost saving was obtained from scheduled load with PV under FIT policy. About 66% of savings can be achieved by utilizing PV. Also the cost of energy is less for scheduled load than unscheduled load. This is because, in unscheduled load, there is very poor utilization of the produced photovoltaic energy and at the time when PV production is highest, there is very low power demand. Since the customer does not have any energy storage devices, the excess power generated therefore goes to waste.
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