The results of the simulation studies on the test system were presented in this chapter. Using different scenarios, the role of the DRRs in the day-ahead energy market was explored. The use of DRRs benefits the system by bringing about a decrease in the total scheduling cost. The relation between the DRR and its associated payback load and its influence on the DRR offer was also brought into focus. It was found that the magnitude of payback load and the period of its occurrence can hinder the use of DRRs by the system operator.
Multiple DRRs were introduced to address competition between DRRs. The nature of the impact of multiple DRRs on the market clearing price was evaluated.
The use of multiple DRRs is a very effective strategy to combat high prices in the
electricity market. The influence of varying load levels on the use of DRRs and its impact on the market clearing prices were also examined.
CHAPTER 5 CONCLUSION
In this thesis, the UC problem formulation is extended to better incorporate DRRs. The system operator accepts bids from generators to sell and offers from DRRs to curtail, then determines a minimum cost schedule of the available units to meet the energy and capacity-based ancillary services demand.
The key feature of the work in this thesis is the development of a model to explicitly include DRRs, their demand curtailment and the associated payback effect. The model is then used to extend the UC formulation. The advantage of the model developed is that it has the flexibility to allow the effective capture of different kinds of load curtailment and payback scenarios. The extended UC problem is solved using a MILP based solver.
The numerical results provide some insight into the operation of DRRs in a competitive market. One salient feature of the numerical work is the impact of the use of DRRs on the market clearing prices. The presence of DRRs leads to a lowering of the market clearing prices, and the decrease is more pronounced on days with higher levels of system demand. A strong incentive exists for the ISOs to encourage more DRR participation in their markets. The presence of a number of competing DRRs makes it possible to lower energy consumption across peak demand periods, thereby decreasing the price spikes.
However, introduction of the payback effect leads to an increase in the effective cost of using DRRs, and leads to a decrease in the number of successful DRR offers. Some work has been done pertaining to the magnitude and time of the payback load, but detailed study of the payback effect is beyond the scope of this presentation. Such work is left for future research. Since payback load can appear
over periods of low system load, the integration of DRRs and an intermittent energy source like wind energy is another interesting topic for future work.
Developments in the United States have indicated that the use of DRRs is gaining favor among the system operators and the end-users. The measures to be undertaken to encourage DRRs and ensure fair treatment for all the participating resources constitute a difficult challenge for the electric industry. But what is clear is that the electricity market will be required to adjust to the phenomena of active demand-side participation, posing new problems for engineers and offering new avenues for future research.
APPENDIX
The unit characteristics of the generators used in the numerical analysis and their initial states are specified in Table A.1.
TableA.1:Generatingunitdata
148017522904610114.6717528017.26015019.85050 21321574.26610155.012564.720674.4302 3122303106610148.07306256.5503065.03030 4370808702610149.648015058.1015066.84070 5370808702610149.48015058.4015067.16070 6370808702610149.068015057.72015066.38070 7123355106610125.26356329.7203034.18030 82158513206610125.538513530.0306034.53020 961154206610123.17153127.2602531.3505 102934001-10075.3431188.63015101.9203 11680100640441012.141002902.5203002.9090 121102015021-10082.022050130.79040150.56020 131102015011-100112.022050131.79040151.56020 141105015011-10093.595070110.28020126.97020 151105015011-10094.595070111.28020127.97020
TableA.1:Generatingunitdata(continued)
161102015011-100111.832050131.57040151.31020 175022000010110207558.260377200050 181232000010110203059.26058210035 19641200010110122060.26024220020 202050001011051061.260523005 211940001001041062.260524004 22140001100100663.260425004
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