Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers’ energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home’s power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RA ${k}$ EL), and a bespoke multi-label classification algorithm (ML ${k}$ NN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.

Toward Non-Intrusive Load Monitoring via Multi-Label Classification

Summary form only given. The connection of large numbers of small or micro-generators to low voltage distribution networks may put at risk traditional network designs. Such designs are based on traditional assumptions such as historical diversity factors and other simplifying assumptions that may no longer be appropriate. This paper describes on-going research, based on a detailed examination of a typical low voltage distribution system with increasing penetrations of distributed generation, mostly in the form of micro-generators (micro-clip, building integrated wind, and building integrated photovoltaics) connected at domestic properties. Modelling techniques have been developed that are applicable to complete distribution feeders: from primary substations, through medium and low-voltage networks to individual single-phase customer connection points. The modelling uses accurate unbalanced power-flow analysis (load-flow) and 1-minute time-series load data. Provisional results show that statistically, in terms of voltage quality and line limits, higher than expected penetrations of distributed generation can be accommodated without modifications to the distribution system. System evolution is discussed with even higher penetrations in mind.

/pdf/network-power-flow-analysis-for-a-high-penetration-of-tpiy1sb43k.pdf

Network power-flow analysis for a high penetration of distributed generation

A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector

Individual motorized transport is a major source of emissions and needs to be reduced to meet international agreements. Although alternatives to internal combustion engine vehicles are already on the market, without extensive political support, electric vehicle (EV) adoption remains low. Understanding the drivers of adoption of alternative technologies is key to develop effective measures to accelerate their diffusion. This paper presents individual consumer characteristics and home-location based spatial characteristics of current battery electric vehicle (BEV) and internal combustion engine vehicle holders, in a region free from strong EV policies. Using a generalized linear mixed-effects logistic model on this revealed preference data, we find that BEV adoption is predicted by technology affinity, high income, green party preferences, and living in one’s own house. Altogether, the study offers insights on the characteristics of early adopters of BEVs that can be valuable to policymakers, energy grid and charging infrastructure operators, as well as the automotive industry.

Battery Electric Vehicle adoption in regions without strong policies

Integrated HVAC management and optimal scheduling of smart appliances for community peak load reduction

Many Demand Side Management (DSM) approaches use energy prices as steering signals. This paper shows that such steering signals may result in power quality problems and high losses. As an alternative, this paper proposes to use desired (e.g., flat) power profiles as steering signals and presents an efficient scheduling algorithm that can follow desired power profiles. This paper investigates the complexity of price and profile steering, and presents an algorithm for profile steering. The evaluation of this algorithm studies the results of a best possible uniform pricing and profile steering for a case of 121 houses, each with an electrical vehicle of which the power consumption can be controlled and shifted in time. In contrast to the other evaluated approaches, our profile steering algorithm results in a much flatter profile and keeps the voltage between 220V and 235V at each node. It reduces distribution losses by 57% compared to no control, and by 48% compared to uniform pricing.

Demand side management using profile steering

Various Demand Side Management (DSM) approaches have been developed the last couple of years to avoid costly grid upgrades. However, evaluation of these DSM methodologies is usually restricted to a use-case specific example, making comparison between different DSM approaches hard. This paper presents a novel, open source, load profile generator to evaluate and compare DSM approaches. In addition to static load profiles for both active and reactive power, it also provides flexibility information for various classes of controllable domestic devices. Load profiles and flexibility information are generated using a simple behavioural simulation. The output data uses 1 minute intervals and incorporates device measurements. The generated profiles are in sound with the measurement data obtained in a field test on both the household level and aggregated neighbourhood level. The same dynamics, such as unbalanced loading and rapid power consumption fluctuations, are observed in the generated model.

/pdf/generation-of-flexible-domestic-load-profiles-to-evaluate-4oaqwu236u.pdf

Generation of flexible domestic load profiles to evaluate Demand Side Management approaches

Driven by the effects of climate change, our world is in a rapid transition towards a sustainable society powered by renewable energy, such as produced by solar panels and wind turbines. As a result, the supply of energy becomes less controllable, endangering the stability of the electricity system. On the other hand, the adoption of electric vehicles and heat pumps also provides an opportunity to avoid overloading as electricity can be produced and consumed locally. The scope of this thesis is to perform decentralized energy management, specifically within residential distribution grids where large scale adoption of distributed energy resources (DERs) is expected. A cyber-physical systems approach is taken to study the interaction between control systems, the operation of devices and the effect on the physical grid. The first contribution is a proactive control methodology called profile steering to decentralize the coordination between all DERs in a microgrid. The hierarchical structure of profile steering allows for a natural mapping between and radially operated low voltage grids. The model predictive nature of this approach resolves prediction errors in both the time and energy domain. A second contribution is a control methodology based on double-sided auctions, for real-time balancing in islanding situations. In such situations, DERs can assist conventional backup solutions in balancing a microgrid. Local information can be used to avoid overloading when communication networks fail. The third contribution is the developed simulation and demonstration framework to test these control methodologies in a cyber-physical systems context. A real-life stress-test resulted in a supply interruption due to grid overloading. The lack of controllability was a major cause for this, illustrating the importance of control in future distribution grids. The presented decentralized energy management approach is a valuable tool to unlock flexibility of DERs and provide means for a smooth energy transition.

/pdf/a-cyber-physical-systems-perspective-on-decentralized-energy-770i8fkerz.pdf

A Cyber-Physical Systems Perspective on Decentralized Energy Management

To evaluate the impact of the energy transition on distribution grids, a year 2025 scenario stress test was conducted in a real Dutch distribution grid. For this, the authors confronted the local low-voltage grid of a village with 20 electrical vehicles and ovens. The result was a short service interruption which could not be avoided by control. Furthermore, severe unbalance and high peak load were observed, resulting in large voltage drops and severe neutral point shifts. The obtained measurements are used to validate simulation models used to study the effect of novel control strategies to balance the local grid.

/pdf/charging-electric-vehicles-baking-pizzas-and-melting-a-fuse-4902g6z30p.pdf

Charging electric vehicles, baking pizzas, and melting a fuse in Lochem

The number of (hybrid) electric vehicles is growing, leading to a higher demand for electricity in distribution grids. To investigate the effects of the expected peak demand on distribution grids, a stress test with 15 electric vehicles in a single street is conducted and described in this paper. The test is conducted in a neighbourhood where both transformers and households are equipped with measurement devices. A significant maximum power consumption increase (more than double) is observed at one transformer when both the electric vehicles and domestic loads stress the network. The observed voltage drop in the network is 17V. Analysis further shows that the hosting capacity is around 15%–20% for the investigated networks and that under voltage is unlikely to occur. The measurements are compared to a simulation and the results show that the simulations predict the actual measurement accurately.

Impact of peak electricity demand in distribution grids: A stress test

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