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OPTIMAL LOAD SCHEDULING IN COMMERCIAL AND RESIDENTIAL MICROGRIDS
Title
OPTIMAL LOAD SCHEDULING IN COMMERCIAL AND RESIDENTIAL MICROGRIDS
Creator
Ganji Tanha, Mohammad Mahdi
Date
2015, 2015-05
Description
Residential and commercial electricity customers use more than two third of the total energy consumed in the United States, representing a...
Show moreResidential and commercial electricity customers use more than two third of the total energy consumed in the United States, representing a significant resource of demand response. Price-based demand response, which is in response to changes in electricity prices, represents the adjustments in load through optimal load scheduling (OLS). In this study, an efficient model for OLS is developed for residential and commercial microgrids which include aggregated loads in single-units and communal loads. Single unit loads which include fixed, adjustable and shiftable loads are controllable by the unit occupants. Communal loads which include pool pumps, elevators and central heating/cooling systems are shared among the units. In order to optimally schedule residential and commercial loads, a community-based optimal load scheduling (CBOLS) is proposed in this thesis. The CBOLS schedule considers hourly market prices, occupants’ comfort level, and microgrid operation constraints. The CBOLS’ objective in residential and commercial microgrids is the constrained minimization of the total cost of supplying the aggregator load, defined as the microgrid load minus the microgrid generation. This problem is represented by a large-scale mixed-integer optimization for supplying singleunit and communal loads. The Lagrangian relaxation methodology is used to relax the linking communal load constraint and decompose the independent single-unit functions into subproblems which can be solved in parallel. The optimal solution is acceptable if the aggregator load limit and the duality gap are within the bounds. If any of the proposed criteria is not satisfied, the Lagrangian multiplier will be updated and a new optimal load schedule will be regenerated until both constraints are satisfied. The proposed method is applied to several case studies and the results are presented for the Galvin Center load on the 16th floor of the IIT Tower in Chicago.
Ph.D. in Electrical and Computer Engineering, May 2015
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SECURITY-CONSTRAINED UNIT COMMITMENT RESERVE DETERMINATION IN JOINT ENERGY AND ANCILLARY SERVICES AUCTION
Title
SECURITY-CONSTRAINED UNIT COMMITMENT RESERVE DETERMINATION IN JOINT ENERGY AND ANCILLARY SERVICES AUCTION
Creator
Ganji Tanha, Mohammad Mahdi
Date
2012-07-25, 2012-07
Description
This study presents the method in which the energy and ancillary services auction is simultaneously cleared in electricity market. By the...
Show moreThis study presents the method in which the energy and ancillary services auction is simultaneously cleared in electricity market. By the security-constraint unit commitment model proposed in this study Independent System Operators (ISO) can determines the sufficient amount of reserve which is necessary to maintain security and reliability of the system. Before the fixed reserve requirement either equal to a percentage of the system peak load or a thermal unit with highest capacity is considered in energy and ancillary service auction in market clearing. The disadvantage of this method is high cost and insufficiency. When it is insufficient the system operator needs to committee more thermal units or does the load curtailment. At the time the fixed quantity is more than needed customers pay more although it is not necessary. Here the sufficient amount of reserve is determined based on the contingency which has been simulated. Contingencies include thermal unit outage and line outage is considered. The amount of reserves is obtained based on thermal units’ physical constraints and the rate offered in the electricity market. Then the integration of wind generation and its effects on the quantity of the reserve determination is considered. Since the wind power generation brings uncertainties to the power system we need to consider scenarios. In order to generate wind power generation scenarios we use Monte Carlo simulation. Since the number of scenarios are too much and increase the problem complexity we use Fast backward/forward scenario reduction. This problem is solved through direct optimization problem including the minimization of the operational cost as well as satisfying the network security constraints when contingency happens.
M.S. in Electrical Engineering, July 2012
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