الفهرس 
DSM within Utilities
DSM Optimization Algorithm for Energy ManagementOnly 14 pages are availabe for public view
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Abstract
Utilities all over the world consider the wide spread integration of renewable energies in modern power systems which will, reduce cost and minimize the environmental impact. The main problem that faces these kinds of resources is their nature which has changing profile curves of the power generated. Consequently, they have an effect on the ability to face the, needed demand. One of the solutions to solve this mismatching issue between both the needed demand curves and available resource curves is through reshaping the demand curves to match the available source curves.
Utilities use Demand Side Management (DSM) programs to help in reducing their peak power purchases on the wholesale market. Consequently DSM programs can decrease the bill for customers. On the other hand DSM programs limit the cost that utilities needed to rebuild new stations for generation, transmission and distribution.
This thesis proposes implementation for Genetic Algorithm (GA) technique in solving the problem for mismatching of supply and load curves using DSM program. The proposed algorithm is implemented on a day with realistic demand and supply profiles.
The DSM strategy is implemented on the demand taking into consideration all different type of loads which are divided into different types of loads residential, commercial and industrial loads.
The proposed DSM optimization algorithm is carried out in two directions:
1- Scenario A [Load Shifting]:
Case I:
Optimally maximizing the system LF through minimizes the gap area between the supply and the load curves. This process is carried out by shifting the load profile for all residential, commercial and industrial types.
Case II:
Optimally maximizing the system LF through minimizes the gap area between the supply and the load curves. Unlike case I, the load shifting is carried out for all types while taking into consideration two different Time of Use (ToU) tariff.
Case III:
Optimally maximizing the system LF through minimizes the gap area between the supply and the load curves. Unlike case I and case II, the load shifting is carried out based on a priority matrix (i.e. only for industrial type loads) and ToU constrains.
2- Scenario B [Valley Filling - Plug-in Hybrid Electric Vehicles (PHEVs)]:
Case IV:
If the load shifting is not applicable, thus PHEVs can be used to supply the peak demand (i.e. supplying the shortage in the given utility supply at peak loads). This discharge is carried out at high prices while they are being charged (as loads) at off peak load (i.e. at low prices). Therefore, case IV optimally shifting the PHEVs charging time to minimize the charging cost while satisfying the required peak loads.
This thesis proposes solving the mismatch problem between the demand profiles and available sources profiles using GA technique. The obtained results from this thesis can be, generalized to be applied in, countries based on their load and available supply profiles. The countries can use the algorithm that proposed in this thesis to obtain the best match between the demand profiles and available sources profiles to maximizing the use of their renewable energy sources, maximizing the economic benefit and reducing their peak load demand.