Probabilistic load forecasts provide comprehensive information about future load uncertainties. In recent years, many methodologies and techniques have been proposed for probabilistic load ...forecasting. Forecast combination, a widely recognized best practice in point forecasting literature, has never been formally adopted to combine probabilistic load forecasts. This paper proposes a constrained quantile regression averaging (CQRA) method to create an improved ensemble from several individual probabilistic forecasts. We formulate the CQRA parameter estimation problem as a linear program with the objective of minimizing the pinball loss and the constraints that the parameters are nonnegative and summing up to one. We demonstrate the effectiveness of the proposed method using two publicly available datasets, the ISO New England data and Irish smart meter data. Comparing with the best individual probabilistic forecast, the ensemble can reduce the pinball score by 4.39% on average. The proposed ensemble also demonstrates superior performance over nine other benchmark ensembles.
Multiple energy systems (MESs) bring together the electric power, heat, natural gas, and other systems to improve the overall efficiency of the energy system. An energy hub (EH) models an MES as a ...device with multiple ports using a matrix coupling the inputs and outputs. This paper proposes a standardized matrix modeling method based on the concept of EH to build the coupling matrix automatically. The components and the structure of MES are first defined using graph theory. Then, the matrices describing the topology of the MES and the characteristics of the energy converters are developed. On this basis, the energy flow equations are formulated. Gaussian elimination can then be applied to obtain the coupling matrix and analyze the degree of freedom of the EH. A standard data structure for basic information on the MES is proposed to facilitate computerized modeling. Further, extension modeling of energy storage and demand response is also discussed. Finally, a case study of a modified tri-generation system is conducted to illustrate the proposed method.
Battery participants in performance-based frequency regulation markets must consider the cost of battery aging in their operating strategies to maximize market profits. In this paper, we solve this ...problem by proposing an optimal control policy and an optimal bidding policy based on realistic market settings and an accurate battery aging model. The proposed control policy has a threshold structure and achieves near-optimal performance with respect to an offline controller that has complete future information. The proposed bidding policy considers the optimal control policy to maximize market profits while satisfying the market performance requirement through a chance-constraint. It factors the value of performance and supports a tradeoff between higher profits and a lower risk of violating performance requirements. We demonstrate the optimality of both policies using simulations. A case study based on the PJM Interconnection LLC (PJM) regulation market shows that the approach is effective at maximizing operating profits.
Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers ...implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.
Distributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the ...distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high.
Residential and small commercial consumers could use distributed energy storage devices to reduce their electricity bills under variable electricity prices to integrate domestic photovoltaic ...generation, store excess energy produced, and participate in demand response. However, the high purchase price of these devices still limits their applications. This paper introduces an alternative form of distributed energy storage, cloud energy storage (CES), which is a shared pool of grid-scale energy storage resources that provides storage services to small consumers. The goal of this approach is to lower the cost of energy storage by exploiting the complementarity of consumers as well as economies of scale. This paper considers the investment and operating decisions of both the CES operator and the consumers to demonstrate the benefits of this form of storage. Numerical results based on load and electricity prices of residential consumers from Ireland show that CES can be profitable and that CES can benefit consumers by providing energy storage services at a lower cost.
Each stakeholder in a power system not only carries some risk, but also creates risks for others and has the ability to mitigate these risks. For some, these risks are purely financial. For others ...the major concern is the socio-economic risk of an outage, which is not easily translated in monetary terms. This paper analyzes how the interactions of these participants across a complex physical system affect their risk exposure as well as the benefits they derive from this system. It also argues that charging the cost of mitigation measures back to the parties that create outage risks might ultimately reduce this risk and its associated cost. Rules and techniques to implement this approach should be developed and tested. Finally, it proposes directions for research on how making power system operation more risk-aware would help enhance reliability, control cost and facilitate the integration of variable energy resources.
Rechargeable lithium-ion batteries are promising candidates for building grid-level storage systems because of their high energy and power density, low discharge rate, and decreasing cost. A vital ...aspect in energy storage planning and operations is to accurately model the aging cost of battery cells, especially in irregular cycling operations. This paper proposes a semi-empirical lithium-ion battery degradation model that assesses battery cell life loss from operating profiles. We formulate the model by combining fundamental theories of battery degradation and our observations in battery aging test results. The model is adaptable to different types of lithium-ion batteries, and methods for tuning the model coefficients based on manufacturer's data are presented. A cycle-counting method is incorporated to identify stress cycles from irregular operations, allowing the degradation model to be applied to any battery energy storage (BES) applications. The usefulness of this model is demonstrated through an assessment of the degradation that a BES would incur by providing frequency control in the PJM regulation market.
Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) ...inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach.
Transmission expansion and energy storage increase the flexibility of power systems and, hence, their ability to deal with uncertainty. Transmission lines have a longer lifetime and a more ...predictable performance than energy storage, but they require a very large initial investment. While battery energy storage systems (BESS) can be built faster and their capacity can be increased gradually, their useful life is shorter because their energy capacity degrades with time and each charge and discharge cycle. Additional factors, such as the expected profiles of load and renewable generation significantly affect planning decisions. This paper proposes a stochastic, multistage, coplanning model of transmission expansion, and BESS that considers both the delays in transmission expansion and the degradation in storage capacity under different renewable generation and load increase scenarios. The proposed model is tested using a modified version of the IEEE-RTS. Sensitivity analyses are performed to assess how factors such as the planning method, the storage chemistry characteristics, the current transmission capacity, and the uncertainty on future renewable generation and load profiles affect the investment decisions.
PDF
