Overall description

With reference to the above economic concepts, the whole procedure for pool-based DRX market clearing is described in Fig. 3.3. It includes several operational stages reflecting different requirements for scheduling DR from a practical point of view. These stages are

Market clearing optimization model

Figure 3.3:Flowchart for clearing a DRX market

coordinated centrally by the DRXO using data collected from market participants, who either request or supply DR resources. The proposed market clearing procedure operates on an hourly basis, to synchronize with the common timeframe of economic dispatch in the power system.

Stage 1 is rather simple waiting forany market participant among the Transco, Discos and Recos to request DR from electricity customers during a given hour. Such a request is made as the participant needs a certain amount of load reduction to deal with its peak demand problems relating to electricity market volatility or power network reliability. For example, when the Transco and Discos foresee network outages, they could impose load curtailment to mitigate the outage consequences. Similarly a Recos may anticipate some upcoming spikes of the wholesale electricity market price and thus have to reduce customer demand by requesting DR. Note that not (necessarily) all participants wants DR in a given

on-peak hour, but if none of them requesting, all subsequent tasks of the DRX market clearing (see Fig. 3.3) would not be undetaken for that hour.

Following buyer requests, those electricity customers capable of curtailing loads register to sell DR on the market. This registration is voluntary as it is decided by the customers on their own free wills. In the case that no customer register, no DR is available for supply and consequently the market would not be cleared. This case, however, is rare since there are always customers interested in selling DR as a source of income [27]. Then the market clearing can be preceded by matching this supply availability with buyer demands.

For those customers registering for selling DR, their “baseline” electricity consumption will be estimated by the DRXO (see Fig. 3.3, stage 3). This consumption refers to the amount a customer wouldnormally use, and is considered for calculating DR in the form of load curtailment during peak demand. Generally estimating the baseline consumption entails considering both load forecasting and baseline manipulation issues.

Electricity load forecasting

Forecasting is vital part of business planning in today’s competitive electricity markets. Many operating decisions are essentially based on load forecasts, i.e., dispatch scheduling of generating capacity, reliability assessment, and maintenance planning for the generators, as well as DRX market clearing. While much work has been done for forecasting aggregated loads coming from customers in groups, less attention being paid to individual customers [68]. Without an accurate individualized prediction model, not only DRX but also other scheduling programs involving small customers will not work realistically.

Forecasting individual loads are generally more difficult than at aggregated levels [72]. This can be explained byabnormal electricity consumption (such as when the consumer is on vacation), which bias analysis of historical consumption behavior, and thus signifi- cantly decrease the prediction accuracy (note that at aggregated levels, such abnormality effects are dominated by the large number and the geographical dispersion of customers, as per central limit theorem in probability theory.) To deal with this forecasting problem, anomaly detection has been developed using statistical techniques such as regression-based, entropy-based, and clustering-based [74]. These anomaly detection methods could be incor- porated within conventional load forecasting models, to deal with the inherent uncertainty in individual consumption behaviour.

Baseline manipulation

The above load forecasting techniques are typically used by an electric utility company having insufficient consumption data from private customers [74]. To be more realistic in forecasting while preserving privacy, each customer can estimate their own consumption and send this information back to the utility. In this scenario, load prediction will definitely be improved, but it could be vulnerable to baseline manipulation where the customers “lie

Market clearing optimization model

Figure 3.4:Verifying DR dispatch by a customer

on purpose” by declaring overestimated baseline consumption, with the aim of claiming more load curtailment than the actual amount and thus illegitimately increasing the mon- etary compensation. No doubt, this baseline issue has to be addressed properly for reliable and accurate load forecasting, but is beyond the scope of this thesis.

Following baseline estimation for electricity customers registering in DR supply, the market can be cleared centrally by the DRXO using an optimization model (to be devel- oped in next subsections.) Then DR will be dispatched virtually by which the customers switch off their loads.

The final step in Fig. 3.3 is measurement and verification to ensure that customers have supplied right amounts of DR following above market clearing results. These tasks are illustrated in Fig. 3.4, where the load curtailment amount representing DR is measured as the difference between baseline (estimated above) and actual consumption which can be metered using a telemetry system called “Advanced Metering Infrastructure” or simply AMI installed on the customer site and communicating with the host utility company [22]. There are many AMI standards developed by different hardware manufacturers over the world. These are characterized in terms of core functionalities and implementation costs as of Table 3.2 (see [68] for a comprehensive review). Since these AMI technologies have been successfully tested by many utilities in North America, Europe, and Australia, they can be utilized for DRX operation generally, and dispatch measurement and verification in particular.

In the following we use microeconomic theory defined in [5, 6, 75, 77–79] to develop the core optimization model for DRX market clearing given by step 4 in Fig. 3.3. For the sake of simplicity, we defer discussion of the background theory until required during the analysis.

Table 3.2:Some well-developed AMI standards

Name Countries

open Automated Demand U.S.A

Response (openADR) (i.e., California.) Google PowerMeter U.S.A, Australia,

Europe. ZigBee/HomePlug Australia, Smart Energy Profile Europe.