Wooyoung Jeon

Is Deferrable Demand an Effective Alternative to Upgrading Transmission Capacity

AbstractWith high penetrations of variable generation from wind turbines in remote locations, transmission capacity may be inadequate to transfer this relatively inexpensive source of generation to demand centers. The major reason is that transmission corridors into load centers are often congested when the system load is high, and additional wind generation is effectively shut out. In contrast, when the system load is low and the wind is blowing, wind generation may be able to meet most of the load throughout the network subject to the specific limitations of the network’s topology. This paper compares the system costs of two very different ways of reducing congestion on the network to increase the annual amount of potential wind capacity dispatched. The first way uses the standard supply side solution of upgrading transmission capacity on the network. The second way uses a demand-side approach in which deferrable demand shifts the system load from on-peak periods to off-peak periods. In addition, the de...

The Effect of Stochastic Wind Generation on Ramping Costs and the System Benefits of Storage

The objective of this paper is to demonstrate 1) how adding storage capacity to a network can mitigate the variability of wind generation and increase the system benefits, and 2) how the stochastic characteristics of the wind generation affect the system benefits of storage capacity. Two types of storage are considered. One represents utility-scale storage that is collocated at the wind sites, and the other represents an identical amount of deferrable demand located at load centers. The simulation is based on a multi-period, stochastic, Security Constrained Optimal Power Flow (SCOPF) and a reduction of the NPCC network. The results demonstrate that storage capacity can dispatch more wind, mitigate the ramping costs associated with wind variability, and reduce the amount of reserve capacity needed. Deferrable demand can further enhance the system operation, by flattening the typical daily pattern of load, reducing the peak system load and reducing the amount of installed capacity needed on the supply side.

Barriers to Increasing the Role of Demand Resources in Electricity Markets

The objective of this paper is to show that customers can benefit from a smart grid if they become more active participants in electricity markets by 1) relying more on deferrable demand (e.g. electric vehicles and augmenting space conditioning with thermal storage) to shift demand away from peak periods and buy more electricity when prices are low at night, and 2) selling ancillary services such as ramping capacity to mitigate the inherent uncertainty of wind generation. These two factors, coupled with the lower operating cost of wind generation compared to conventional generation from fossil fuels, have the potential for reducing the cost of electricity to customers. However, these benefits will not be realized unless the rates charged to customers reflect the true costs of supply. This paper compares how the bills charged to different types of customer are affected by different rate structures with and without the correct economic incentives. The main savings in operating cost come from the displacement of conventional generation by wind generation, and the main savings in capital cost come from reducing the amount of installed conventional generating capacity needed to maintain System Adequacy by 1) reducing the peak system load, and 2) by using deferrable demand to provide ramping services and reduce the amount of conventional generating capacity needed for operating reserves. A new stochastic form of multi-period Security Constrained Optimal Power Flow is applied in a simulation using a reduction of the North Eastern Power Coordinating Council (NPCC) network for a representative summer day. This model treats potential wind generation as a stochastic input and determines the amount of conventional generating capacity needed to maintain reliability endogenously. The analysis assumes implicitly that all deferrable demand at a node is managed by an aggregator. If the rates are structured with the correct economic incentives (i.e. real-time nodal prices for energy, a demand charge determined by the demand during system peak periods, and compensation for providing ramping services), the results show that 1) the economic benefits for customers with thermal storage are substantial, and 2) the main benefits for customers with electric vehicles (without V2G capabilities in this application) come from buying less gasoline. In contrast, if customers pay conventional rates with a fixed price for energy and no demand charge, the economic incentives are perverse and customers with deferrable demand pay more and customers with no deferrable demand pay less.

Can Energy Bids from Aggregators Manage Deferrable Demand Efficiently

Our previous research has shown that distributed storage capacity at load centers (e.g. Deferrable demand) controlled by a system operator can lower total system costs by smoothing out and flattening the daily dispatch profile of conventional generating units and providing ramping services. Since it is in reality impractical for system operators to control large numbers of customers with deferrable demand directly, aggregators will in all likelihood be responsible for managing the individual sources of deferrable demand using instructions provided by a system operator. The objective of this paper is to compare the performance of deferrable demand when 1) the aggregators act as clients to the system operator and receive physical charge/discharge instructions for managing deferrable demand (i.e. Centralized control), with 2) the aggregators follow their own interests and submit bids for purchasing energy into the wholesale auction using projected prices provided by the system operator (i.e. Hierarchical control). The analysis uses a stochastic form of multi-period Security Constrained Optimal Power Flow (SCOPF) in a simulation using a reduction of the Northeast Power Coordinating Council (NPCC) network for representative days. This model treats potential wind generation and load as stochastic inputs and determines the optimum daily profiles of dispatch and demand for different realizations of hourly wind generation and load. Ramping capacity is acquired to ensure that transitions from the realizations in one hour to the next hour, as well as contingencies, can be supported. The results show that if aggregators receive stochastic forecasts of energy prices for the next 24 hours, their optimum strategy for minimizing the expected cost of their purchases from the grid is to determine a high threshold price for discharging and a low threshold price for charging, and as a result, they provide ramping services as well as benefitting from day/night price arbitrage. However, the results are sensitive to the form of the price forecasts.

The Case for a Simple Two-Sided Electricity Market

This paper builds on the results from our earlier research on the design of electricity markets that have to accommodate the uncertainty associated with high penetrations of renewable sources of energy. The key results show that 1) distributed storage (deferrable demand) is an effective way to reduce total system costs, 2) a simple market structure for energy allows aggregators to meet their customers’ energy needs and provide ramping services to the system operator, and 3) using a receding-horizon optimization to dispatch units for the next market time-step benefits from the availability of more accurate forecasts of renewable generation and allows market participants to adjust their bids and offers in response to this new information. In our two-sided market, distributed storage in the form of deferrable demand is controlled locally by independent aggregators to minimize their expected payments for energy in the wholesale market, subject to meeting the energy needs of their customers. In addition, these aggregators are responsible for maintaining a stable power factor by installing local capabilities that automatically deal with local power imbalances. Failure to do this triggers penalties paid to the system operator. Our earlier results have shown that it is optimal for an aggregator to submit demand bids into a day-ahead market that include threshold prices for charging and discharging storage and also ensure that the expected amount of stored energy is consistent with the capacity limits of their storage. Because departures from the expected daily pattern of renewable generation are generally persistent (highly positive serial correlated), it is likely that the system operator determines an optimum pattern of demand for the aggregator that violates the capacity limits of storage by the end of the 24-hour period. If the market uses a receding horizon, the results in this paper show that aggregators can modify their bids to ensure that the capacity limits of storage are never violated in the next market time-step. In an empirical application, a stochastic form of multi-period security constrained unit commitment with optimal power flow (the MATPOWER Optimal Scheduling Tool, MOST) using a receding-horizon optimization determines the optimum dispatch and reserves for the next hour and forecasts of the nodal prices for the next 24 hours. The results show that locally controlled deferrable demand is almost as effective as centrally controlled deferrable demand as a way to reduce system costs and mitigate the variability of renewable generation. The additional advantage from using a receding horizon is that the system operator always charges/discharges the storage managed locally by aggregators within the capacity constraints of the storage.