Search for: All records

The rapid growth of the behind-the-meter (BTM) distributed generation has led to initiatives to reform the net energy metering (NEM) policies to address pressing concerns of rising electricity bills, fairness of cost allocation, and the long-term growth of distributed energy resources. This article presents an analytical framework for the optimal prosumer consumption decision using an inclusive NEM X tariff model that covers existing and proposed NEM tariff designs. The structure of the optimal consumption policy lends itself to near closed-form optimal solutions suitable for practical energy management systems that are responsive to stochastic BTM generation and dynamic pricing. The short and long-run performance of NEM and feed-in tariffs (FiT) are considered under a sequential rate-setting decision process. Also presented are numerical results that characterize social welfare distributions, cross-subsidies, and long-run solar adoption performance for selected NEM and FiT policy designs. 

We introduce NEM X, an inclusive retail tariff model that captures features of existing net energy metering (NEM) policies. It is shown that the optimal prosumer decision has three modes: (a) the net-consuming mode, where the prosumer consumes more than its behind-the-meter distributed energy resource (DER) production when the DER production is below a predetermined lower threshold, (b) the net-producing mode where the prosumer consumes less than its DER production when the DER production is above a predetermined upper threshold, and (c) the net-zero energy mode where the prosumer’s consumption matches to its DER generation when its DER production is between the lower and upper thresholds. Both thresholds are obtained in closed-form. Next, we analyze the regulator’s rate-setting process that determines NEM X parameters such as retail/sell rates, fixed charges, and price differentials in time-of-use tariffs’ on and off-peak periods. A stochastic Ramsey pricing program that maximizes social welfare subject to the revenue break-even constraint for the regulated utility is formulated. The performance of several NEM X policies is evaluated using real and synthetic data to illuminate the impacts of NEM policy designs on social welfare, cross-subsidies of prosumers by consumers, and payback time of DER investments that affect long-run DER adoptions. 

distributed random variables with which the original time series has a causal representation. The innovation at a time is statistically independent of the history of the time series. As such, it represents the new information contained at present but not in the past. Because of its simple probability structure, the innovations sequence is the most efficient signature of the original. Unlike the principle or independent component representations, an innovations sequence preserves not only the complete statistical properties but also the temporal order of the original time series. A long-standing open problem is to find a computationally tractable way to extract an innovations sequence of non-Gaussian processes. This paper presents a deep learning approach, referred to as Innovations Autoencoder (IAE), that extracts innovations sequences using a causal convolutional neural network. An application of IAE to the one-class anomalous sequence detection problem with unknown anomaly and anomaly-free models is also presented.