The future of efficient EMS lies in improved demand side management which is largely influenced by DR programs. Demand response is envisaged as one of the most strategic solutions for the 21st century power system already battered
by limited resources, increased environmental threats as well as price-spike power demands [28]. According to Federal Energy Regulatory Commission (FERC) in the US, it is defined as deliberate modifications of electric usage by end-use customers in response to price changes of electricity with time, in order to receive associated incentive payments due to reduced energy use during high wholesale market prices or during jeopardized system reliability [29]. Due to the acknowledgement about how strategic DR is, several governments have continued to encourage and support programs and schemes that encourage improved and active participation in DR programs. Several writers have also contributed in developing methodologies that enhance improved DR programs for improved participation by users.
In [30] the authors designed a Dynamic Demand Response Controller (DDRC) which was implemented in MATLAB/SIMULINK and connected to EnergyPlus model via building controls virtual test bed (BCVTB) so that one can control Heating, Ventilation and Air Conditioning (HVAC) loads using RTP information and ambient temperature values. The justification for the research was the need to participate more actively and more economically to DR programs via RTP information rather than relying on dynamic-controlled thermostats supplied by Austin Energy in Texas USA, which were capable of switching HVAC loads on/off depending on ambient indoor temperature. This DDRC control was achieved by designating a customer-specific threshold retail price, which was to be compared with the real-time-price of electricity. If the RTP is above the threshold, DDRC changes the set-point temperature of the thermostat in line with the prevailing price difference. With a hypothetical RTP data estimated
every 15 minutes, DDRC result showed a reduction of 12% and 21% in price of electricity for heating and cooling, in the months of January and August which represented the coldest and warmest months of the year, respectively.
The authors In [31] proposed a methodology for management of Distributed Energy Resources (DER) within the Supervisory Control And Data Acquisition (SCADA) systems by scheduling of the generation units in order to maximize the performance of the energy supply. The optimal operation of the variables which are: distributed generation, DR and storage resources, was formulated as a Generalized Branch-and-Cut (GBC) Mixed Integer Linear Programming (MILP) model and solved in general algebraic modelling systems (GAMS) platform using CPLEX optimizer. In a case study presented, the objective function of the MILP is the total cost for a given period (T) and was minimized [31]. Result showed that using an intelligent and flexible SCADA, existing resources are utilized by agents that require them in a robust and efficient way. Although the work as presented by the authors focused on distributed generation, this thesis is based on effective home energy management systems so these methods are not used here. However, it is important to highlight what other researchers are doing in this area since some energy consumers are also producers hence, information about applications of distributed generation becomes relevant.
In [32] the paper discussed the response to electricity spot prices (or RTP) for storage-type customers who are capable of participating actively in demand- side response programs as prosumers. This means that as well as being a
consumer, such customers also have the capacity to become energy producers. The responses were classified into three categories given as:
Curtailment: Switch off when price goes higher than a certain threshold. Substitution: Switch to alternative supply whenever it is cheaper to do so. Storage: Load scheduling to times of the day when energy costs are lower
and this includes charging energy-storage facilities within such times.
The justification for the research was to establish the advantages of implementing spot pricing of electricity with data available 7 days in advance for every one hour interval, and finding the optimal times to operate domestic appliances in order to achieve optimal energy and cost savings. The cost minimization problem was presented as a linear programming formulation, written in APL*PLUS/PC programming language on an IBM PC and solved using a non-simplex algorithm as proposed by Daryanian [32]. Result from a case study showed savings obtained as a result of the difference between the avoided costs of using electricity at higher price-hours minus additional cost at the substituted lower price- hours.
Interestingly, the authors in [33] argued that the efficiency achievable while implementing DR programs is largely affected by the reserve requirement of the system with respect to whether curtailment (peak price clipping) is required, or whether supply from storage is applied. This is because at lower load levels within the load profile, price curtailment is difficult to attain since energy supply is at off-peak demand. This is unlike if the supply during this period is from storage facilities whereby there is no observed effect in obtaining supply from
such storage. Hence the dynamic pricing at any given time will be the factor at any given time to determine when best it is to obtain supply from the storage and it is up to the participating consumers to either tolerate probable power curtailment or to provide actual curtailment for demand reduction. The problem was formulated as a Unit Commitment (UC) problem under a mixed integer problem framework whereby the objective function is a minimization of system total cost which comprised of three components given as: operation cost, reserve cost and expected load-not-supplied cost [33]. Results showed that the technique which is a function of several parameters like load reliability requirements, available DR resource, bidding price of services such as spinning reserve as well as peak clipping can enhance reliability of the system and also improve its economic value.
Finally, the incentives offered in DR programs are usually the motivation for end-user participation and for it to be optimally implemented, adequate awareness is required to enable customers understand how to participate. Also an appropriate application platform is required such that the end-users can easily be integrated into such programs at a minimal cost.