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A prior knowledge of residential load demand is critical for power system operations at the distribution level, such as economic dispatch, demand response and energy storage schedule. However, as residential customers perform more casual and active consumption behaviors, prediction of such highly volatile loads can be much harder. Owing to the development of sensor technology, micrometeorological data can be sampled with a high geographic resolution. Those data that represent the weather condition on the land surface show a strong relationship to the residential load evidently, whereas it remains unsolved on how to fully utilize those great number of datasets. This paper proposes a day-ahead probabilistic residential load forecasting method based on a novel deep learning model, named convolutional neural network with squeeze-and-excitation modules (CNN-SE), and micrometeorological data. The model can employ multi-channel input data with dissimilar weights, suitable for analyzing massive relevant input factors. A feature extraction method is adopted for customer consumption pattern based on sparse auto-encoder (SAE), which can help correct probabilistic forecasting results. A case study that covers 8 residential communities and 18 micrometeorological sites is conducted to validate the feasibility and accuracy of the proposed hybrid method. [ABSTRACT FROM AUTHOR]

According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users’ willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers’ costs and improving the sharing of the PV energy. [ABSTRACT FROM PUBLISHER]

We consider the problem of distributed generation and demand control for primary frequency regulation in power networks, such that stability and optimality of the power allocation can be guaranteed. It was shown in Part I of this work, that by imposing an input strict passivity condition on the net supply dynamics at each bus, combined with a decentralized condition on their steady-state behavior, convergence to optimality can be guaranteed for broad classes of generation and demand control dynamics in a general network. In this paper, we show that by taking into account additional local information, the input strict passivity condition can be relaxed to less restrictive decentralized conditions. These conditions extend the classes of generation and load dynamics for which convergence to optimality can be guaranteed beyond the class of passive systems, thus, allowing to reduce the conservatism in the analysis and feedback design. [ABSTRACT FROM PUBLISHER]

There are few systematic methodologies capable of predicting and leveraging the reserve capacity potential of large populations of thermostatically-controlled loads (TCLs). For such reserves to have economic and technical value, it is essential that demand-side flexibility aggregators and system operators be able to do so quickly and robustly. In addition, aggregators need to ensure TCL users' comfort and end use satisfaction. Part I of this two-part paper introduces an analytical approach to characterize and control the statistical bounds on the potential aggregated response of a population of TCLs, while ensuring users' comfort satisfaction. First, the uncertainty associated with the instantaneous power consumption of a typical TCL is described by a set of random variables and their statistics. TCL statistics are then employed to characterize the exploitable flexibility from a large population of similar devices. From this, a control strategy and parameters are introduced for sporadic (i.e., contingency-type) reserve provision by the population. In Part II, the proposed analytical approach and control strategy are validated for the special case of electric water heaters (EWHs). Further, the trade-off between demand response capacity and EWH users' comfort satisfaction is investigated through several case studies. [ABSTRACT FROM AUTHOR]

We study the problem faced by an operator who aims to allocate a certain amount of load adjustment (either load reduction or increment) to multiple consumers so as to minimize the aggregate consumer disutility. We propose and analyze a simple uniform-price market mechanism where every consumer submits a single bid to choose a supply function from a group of parameterized ones. These parameterized supply functions are designed to ensure that every consumer's load adjustment is within an exogenous capacity limit that is determined by the current power system operating condition. We show that the proposed mechanism yields bounded efficiency loss at a Nash equilibrium. In particular, the proposed mechanism is shown to achieve approximate social optimality at a Nash equilibrium, if the total capacity limit excluding the consumer with the largest one is much larger than the total amount of load to be adjusted. We complement our analysis through numerical case studies. [ABSTRACT FROM PUBLISHER]

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts. [ABSTRACT FROM PUBLISHER]

This paper proposes a security-constrained forward market clearing algorithm within which the inherent characteristics of demand flexibility are acknowledged when the provision of reserve from the demand side is considered. In the proposed formulation, we co-optimize the cost of scheduling the appropriate resources to guarantee the security of the system and the expected cost of operating under any credible system state. In addition, we consider that consumers can offer to provide spinning reserve capacity deployable by voluntary load reductions in response to contingencies. Due to the load recovery effect (i.e., since energy usage is essentially postponed when demand-side reserve is deployed), in post-contingency states, any voluntary reduction in the load has to be accompanied by a subsequent increase in demand from the initial forecast. We demonstrate that the marginal value of the reserve provided by the demand can only be calculated taking into account the cost of supplying the recovery consumption. Flexible consumers can reduce their payments on average if energy is settled through real-time prices. [ABSTRACT FROM PUBLISHER]

Short-term load forecasting is an essential instrument in power system planning, operation, and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the other hand, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. [ABSTRACT FROM PUBLISHER]

Recent work in the field of distribution system analysis has shown that the traditional method of peak load analysis is not adequate for the evaluation of emerging distribution system technologies. Voltage optimization, demand response, electric vehicle charging, and energy storage are examples of technologies with characteristics having daily, seasonal, and/or annual variations. In addition to the seasonal variations, emerging technologies such as demand response and plug-in electric vehicle charging have the potential to receive control signals that affects their energy consumption. To support time-series analysis over different time frames and to incorporate potential control signal inputs, detailed end-use load models that accurately represent loads under various conditions, and not just during the peak load period, are necessary. This paper will build on previous end-use load modeling work and outline the methods of general multi-state load models for distribution system analysis. [ABSTRACT FROM PUBLISHER]

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns. [ABSTRACT FROM AUTHOR]

This paper is the first of a two-part paper on the application of selective direct control of loads for small-disturbance angle stability enhancement in power systems. The change in the utility business model and developments in enabling technologies open up new possibilities for power system reliability enhancement. This paper proposes preventive modulation of selected loads through direct control for small-disturbance angle stability enhancement. An analysis framework based on the structured singular value theory has been proposed in which the controllable load range at selected buses that satisfies the desired stability robustness performance criterion is determined. Two conceptually different approaches for analysis of load modulation have been presented. Results have been presented on an equivalent of the Western Electric Coordinating Council system in the second part of this paper. [ABSTRACT FROM AUTHOR]

This paper develops a state-queuing model to analyze the price response of aggregated loads consisting of thermostatically controlled appliances (TCAs). Assuming a perfectly diversified load before the price response, we show that TCA setpoint changes in response to the market price Will result in a redistribution of TCAs in on/off states and therefore change the probabilities for a unit to reside in each state. A randomly distributed load can be partially synchronized and the aggregated diversity lost. The loss of the load diversity can then create unexpected dynamics in the aggregated load profile. Raising issues such as restoring load diversity and damping the peak loads are also addressed in this paper. [ABSTRACT FROM AUTHOR]