Abstract-Load modelling attracts renewed interests these days in maintaining peak load conditions, supplying new types of loads and accommodating more renewable generation into electricity networks. This work describes real measurement data acquisition and step-by-step signal processing for developing aggregate load models at 11kV and 6.6kV level. Challenges in analyzing real measurement data are highlighted and issues to improve measurement-based load modelling are discussed. Load models at 15 substations from a UK distribution network are presented with subsequent model parameters. These load models will provide an insight to the operational flexibility, network resilience and management requirements of the measurement sites and related up-and-downstream substations.Index Terms--Distribution system, load-voltage relationship, measurement-based load model, operational flexibility.

Abstract-Dependable power quality (PQ) monitoring is crucial for evaluating the impact of smart grid developments. Monitoring schemes may need to cover a relatively large network area, yet must be conducted in a cost-effective manner. Real-time communications may not be available to observe the status of a monitoring scheme or to provide time synchronization, and therefore undetected errors may be present in the data collected. This paper describes a process for automatically detecting and correcting errors in PQ monitoring data, which has been applied in an actual smart grid project. It is demonstrated how to: unambiguously recover from various device installation errors; enforce time synchronization between multiple monitoring devices and other events by correlation of measured frequency trends; and efficiently visualize PQ data without causing visual distortion, even when some data values are missing. This process is designed to be applied retrospectively to maximize the useful data obtained from a network PQ monitoring scheme, before quantitative analysis is performed. This work therefore ensures that insights gained from the analysis of the data-and subsequent network operation or planning decisions-are also valid. A case study of a UK smart grid project, involving wide-scale distribution system PQ monitoring, demonstrates the effectiveness of these contributions. All source code used for the paper is available for reuse.

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Abstract-Dependable power quality (PQ) monitoring is crucial for evaluating the impact of smart grid developments. Monitoring schemes may need to cover a relatively large network area, yet must be conducted in a cost-effective manner. Real-time communications may not be available to observe the status of a monitoring scheme or to provide time synchronization, and therefore undetected errors may be present in the data collected. This paper describes a process for automatically detecting and correcting errors in PQ monitoring data, which has been applied in an actual smart grid project. It is demonstrated how to: unambiguously recover from various device installation errors; enforce time synchronization between multiple monitoring devices and other events by correlation of measured frequency trends; and efficiently visualize PQ data without causing visual distortion, even when some data values are missing. This process is designed to be applied retrospectively to maximize the useful data obtained from a network PQ monitoring scheme, before quantitative analysis is performed. This work therefore ensures that insights gained from the analysis of the data-and subsequent network operation or planning decisions-are also valid. A case study of a UK smart grid project, involving wide-scale distribution system PQ monitoring, demonstrates the effectiveness of these contributions. All source code used for the paper is available for reuse.

Under the current UK regulatory framework for electricity distribution networks, asset upgrades are planned with the objectives of minimising both capital costs (and thus customer fees) and social costs such as those associated with carbon emissions and customer interruptions. This approach naturally results in economic trade-offs as network solutions meant to reduce social costs typically increase (sometimes significantly) capital costs, and vice versa. This can become an issue in a smart grid context where new operational solutions such as Demand Response (DR) may emerge. More specifically, even though there is a general belief that smart solutions will only provide benefits due to their potential to displace investments in costly assets (e.g., lines and substations), they may also introduce trade-offs associated with increased operational expenditure, power losses and emissions compared with networks with upgraded assets. On the other hand, the flexibility inherent in smart solutions could be used to balance the different types of costs, leading to attractive cost trade-offs if properly modelled, quantified and regulated. However, given the fundamental "non-asset" nature of DR, properly quantifying the resulting trade-offs so