Reference [3] shows that in the future smart grid (SG), both users and power companies could benefit from the economic and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price on the customer side. In addition, smart pricing can be used to reduce the peak demand. A wide and comprehensive literature review on peak load reduction is covered by [4].
Reference [5] studies the effectiveness of customer engagement plans that clearly specify the amount of the grid operator''s intervention in the customer load settings for peak load reduction. An adjustable reference temperature for both constant and proportional deviation plans is defined to limit the output temperature of each thermostat load and to control the number of devices eligible for the DR Program. These possibilities offer significant advantages for the entire power system and rewarding incentives to end-users, for their active participation. In this area, energy services and their future development are both a great challenge and a great opportunity for system operators.
The contemporary electricity market enters a new era, focused on environmental protection, energy savings, and the customer-oriented approach. In this context, active participation of customers and their attitude towards consumption represent a valuable tool for a more efficient energy use, changing the demand so as, to allow peak load reductions and load shifting from peak hours, or to the hours with high power generation from renewable energy sources (RES).
DSM remains a significant segment of modern power systems. Its aim is to apply DSO''s activities designed to induce appropriate changes in the consumers` daily load profile shape [6].
The impact of DSM should be assessed by comparison with non-DSM options (those on the suppliers/operator side: building new power facilities, electric power import or energy storage options). During our research, we have applied a similar approach, contrasting the proposed DSM program effects and costs with those of building new power facilities. Technically, DSM determines the way end-users are going to respond (not the way they might respond, as in DR). Finally, the achieved changes in the load profile shape (peak load reduction) determine the benefit of the DSM measures/programs applied.
An effective DSM program introduces the application of energy efficient devices, standard and additional load control equipment, and a two-way communication option. If there is a dynamic relation between end-users and the utility, a system of dynamic control can be established [6].
Reference [7] presents the impact of DSM strategies in the evolution of the electricity mix of Flores Island in the Azores archipelago. The results show that DSM strategies can postpone investment in new renewable energy resources and improve the operation of the existing system. Our own investigations of the Belgrade consumption area, support this conclusion.
Mathematical optimization models of residential consumers are presented in [8]. They can be readily incorporated into automated decision-making technologies in SGs. The modeling problems can then be solved efficiently in real-time to control optimally all major residential energy loads, storage and production components while considering customer preferences and comfort levels. The developed mathematical models result in mixed integer linear programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. The simulation results show significant reduction of both energy costs and the peak demand.
Reference [9] presents that in future power grids load control algorithms can be used to automate the control of certain loads (such as electric vehicles and heating devices) targeting peak reduction, valley filling, minimizing CO2 emissions, load balancing, etc.
Reference [10] investigates the extent to which a home is able to transparently flatten its electricity demand by scheduling their demand for air conditioning (AC) devices, refrigerators, and dehumidifiers with certain flexibility. Results indicate that the average deviation from the mean power is decreased by over 20% across all daily peak periods.
Reference [11] shows that load control applications along with strategically deployed solar photovoltaic (PV) and ice storage systems at the building level can help reduce the building peak demand and energy consumption. The research presents a model for studying coordinated control of building end-use loads, including cooling, lighting and plug loads, together with PV and ice storage integrated with packaged AC units.
In light of the analyzed references, it is obvious that peak load reductions achieved by DR/DSM applications, and their impact on long-term forecasting of the annual peak load, have not been sufficiently investigated. Some of the above topics are addressed in our research, too. Similar to [9,10,11], we propose here a control module for heating and AC devices, in combination with PV panels installed on customers'' roofs. The aim is to optimize the grid operation and to reduce the peak load on both daily and annual levels. As the focus of this paper is on long-term forecasting, we are proposing a method that would solve the negative peak load trend at the time when DR/DSM programs are introduced and expanded.
This paper describes the possibilities to implement different kinds of measures on the demand side, aimed at reducing the peak load of a utility. This reduction can be analyzed within a day, by re-shaping daily load profiles, but the main concern of this paper is the annual peak load reduction as it can postpone investments in new power facilities.
The basic demand-side impacts are the following: direct load control, controlled electricity time-of-use, high pricing of the critical/peak load, and real-time (dynamic) pricing.
The basic six ways for modifying chronological, daily load profiles through demand-side measures/programs are illustrated in Fig. 1. Their application is simulated here by two, typical daily load profiles: by one critical, winter day, and one hot-summer day, both recorded in 2012, for supply area of the Serbian capital – Belgrade. These profiles were obtained by a supervisory control and data acquisition (SCADA) system of the local power distribution utility, EDB.
Possible DR and DSM options for re-shaping typical winter and summer daily load profiles
In Fig. 1, the first three manners of chronological daily load profiles'' change (load shifting, peak shaving and "valley" filling) illustrate traditional goals of profile shape modifications, usually achieved by direct load control. The other three ways (EE, the new, efficient electricity use, and flexible modeling by DR) have been allowed in recent years. They represent, respectively: strategic energy conservation, load growth due to the new, but efficient use of electricity, and flexible load shaping.
In general, DR can be based on incentives given to the end-users, or on time-based tariffs (prices). For effective and efficient implementation of demand-side measures and programs, technology plays a key role. Better market opportunities on the demand side depend on costs, functionalities, and the automation level of processes and technologies, facilitating DR/DSM application.
One of the targets of this paper is to select the best schedule for shiftable load (appliances) in order to reduce the utility peak load. By shifting the load, the energy used by consumers remains the same. Household appliances are categorized as controllable and uncontrollable. The demand of controllable appliances can be shifted. Heating and cooling devices, water heaters, refrigerators and freezers, i.e., the devices fitted with thermostats, are the best candidates for load shifting. Internal thermal storage maintains the temperature within a certain range. Fully automated control of the devices is enabled, within constraints.
The following assumptions are also introduced. Controllable appliances are classified according to their load-shifting flexibility (period of shifting):
15 minutes shiftable loads (refrigerators, etc.) account for 5% of the total controllable load during winter and for 10% during summer;
30 minutes shiftable loads (air-conditioners, etc.) account for 25% of the total controllable load during winter and for 60% during summer;
1 hour shiftable loads (heaters, water heaters) account for 70% of the total controllable load during winter and for 30% during summer.
The assumed percentages and shifting periods are based on the concrete measurements and evaluations made for the case study of EDB. Though the percentages and shifting periods can differ, the essence of our approach here remains the same. The proposed model has been tested with MATLAB software tool. In the simulation, it was taken that shiftable appliances participate with the same percentage every hour.
For a successful application of a DR/DSM program, the first necessary step is to make an accurate estimation of the controllable load (CL) in the given area or utility.
An improved comparison of load profiles, used for the CL estimation, is detailed in [12]. The method is based on comparing typical daily load profiles for the same (or close) date in two different years and different weather conditions. The consequence is that load profiles in these two cases are significantly different. Figures 2 and 3 show two such pairs of load profiles, for winter and summer seasons, respectively. The space between the curves in each figure represents the difference in consumption, i.e., electrical energy used for additional heating in Fig. 2 or cooling in Fig. 3.
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