Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load

Abstract

Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.

Local aggregator enhanced possibilities coupling energy savings and demand response activations

“…Fairness in setting the price is destabilised by the fact that the reduction in consumption cannot be exactly calculated but only estimated. Various load baselining methods simulate the estimated »no event« load, i.e., in absence of a demand response (DR) event [9]. Estimates are primarily based on historical consumption data thus it is necessary to determine the grid user's consumption curve.…”

“…This difference is used for assessing payments and penalties for customers. To avoid manipulations by aggregators, load baselining methods should be realistic and transparent to all flexibility providers [9]. Likewise, to avoid manipulations by users, rules and calculation methods should be well defined.…”

Analysis of Accuracy of Flexibility Baseline Prediction Methods for Office Building at Different Measuring Points

“…The 'window before and after' method considers the measurements before and after the flexibility activation. Like the 'window before' method, the baseline power profile is assumed to be a straight line with a value that is the average of the last measurement before flexibility activation and the measured value after flexibility activation is removed [7]. It is also a simple and transparent method, but with the same disadvantage of manipulation of measurements before and after.…”

A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for Consumers

“…The same methods used for evaluating demanded response programmes can be used for load profile identification [14]. A load profile can also be deducted by segmenting the user's past load profile and appliance activity, as was proposed in [15,16], together with an optimal scheduling of appliances.…”