Search

Traditional wisdom has dictated that in the Northern Hemisphere, residential rooftop photovoltaic (PV) systems should be facing true south when possible to maximize energy production. However, an abundance of south-facing PV systems in communities generating power simultaneously at peak solar times has caused excessive reverse power flows which introduce multiple challenges within the broader power grid. At the aggregate level, this has caused sharp ramps in generation at sunrise and sunset, created challenges for grid reliability, and introduced local grid issues like voltage spikes. As a result, utilities have begun to reduce payments to customers for feeding solar generation back to the grid, moving away from so-called net-metering schemes. This has incentivized west-facing solar panels to better overlap with typical residential demand, reducing reverse flows, but it remains unclear as to how best to choose orientation of panels to achieve community-level goals like self-reliance (e.g., in the case of a microgrid) while minimizing the amount of battery capacity required. In this paper, we consider a community-level design problem for the optimal placement of residential rooftop solar panels considering different home designs and demand patterns. We develop a flexible simulation testbed that solves a mixed-integer linear optimization problem to determine, given a particular community-level objective, the orientation of panel placement within the restrictions of roof geometry and space. The results indicate that optimal placement can help a community maximize self-consumption, facilitating a transition to subhourly net-zero energy goals and reducing the need for energy storage. The following paper includes a novel optimization objective for designing solar panel installations. Whereas historically solar installers have emphasized maximization of solar power generation, utilities have begun to push back on excess power production from so-called prosumers (customers who also generate power) during peak solar production hours. Our proposed objective is to maximize the amount of usable solar power rather than the potential solar power. Solar power that is temporally offset from the conventional south-facing peak production can lessen the impacts of ramp down and ramp up periods. The models presented in this paper result in an optimal ratio of solar panel orientation for a given demand profile and location. We suggest that developers use this information to create a temporally robust solar generation profile in the community. The existing (or designed) roof profiles of a community can be input to the optimization constraints with a limited number of solar panels. The resulting profile assists in reducing the need for rapid ramp up and ramp down capacity as well as reducing battery storage capacity in the event that the community operates in an islanded microgrid mode. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]

Using machine learning to obtain solutions to AC optimal power flow has recently been a very active area of research due to the astounding speedups that result from bypassing traditional optimization techniques. However, generally ensuring feasibility of the resulting predictions while maintaining these speedups is a challenging, unsolved problem. In this letter, we train a neural network to emulate an iterative solver in order to cheaply and approximately iterate towards the optimum. Once we are close to convergence, we then solve a power flow to obtain an overall AC-feasible solution. Results shown for networks up to 1,354 buses indicate the proposed method is capable of finding feasible, near-optimal solutions to AC OPF in milliseconds on a laptop computer. In addition, it is shown that the proposed method can find “difficult” AC OPF solutions that cause flat-start or DC-warm started algorithms to diverge. [ABSTRACT FROM AUTHOR]

Within the United States' National Inventory of Dams, 15,000 dams have been classified as having a high hazard potential, meaning failure or misoperation would lead to probable loss of human life. However, state dam officials evaluate dam hazard potential on a case-by-case basis, ultimately relying on human judgement. Such a process lacks objectivity and consistency across state boundaries and can be time-consuming. Here, the authors present a parameterized geospatial and machine learning dam hazard potential classification model to overcome these limitations. The parameters of this model can be tuned for optimal performance. However, for this classification problem, the regulatory and physical implications of the types of model misclassifications are best captured through multiple objectives. Therefore, this research additionally contributes a novel multiobjective approach to machine learning parameter tuning. This research demonstrates the utility of this approach for dams in Massachusetts, United States, using a multiobjective evolutionary algorithm to explore different model parameterizations and identify analyst-relevant tradeoffs among objectives describing model performance. Such an approach allows for greater justification of model parameters as well as greater insights into the complexities of the dam hazard potential classification problem. [ABSTRACT FROM AUTHOR]

Including complementarity constraints in energy storage system (ESS) models in optimization problems ensure an optimal solution will not produce a physically unrealizable control strategy where there is simultaneous charging and discharging. However, the current approaches to impose complementarity constraints require the use of non-convex optimization methods. In this paper, we propose a convex relaxation for a common ESS model that has terms for both charging and discharging based on a penalty reformulation for use in a model predictive control (MPC) based optimal power flow (DC OPF) problem. In this approach, the complementarity constraints are omitted and a penalty term is added to the optimization objective function. For the DC OPF problem, we provide analysis for the conditions under which the convex relaxation of the complementarity constraint ensures that a solution with simultaneous ESS charging and discharging operation is suboptimal. Simulation results demonstrating ESS behavior with and without the penalty reformulation are provided for an MPC-based DC OPF problem on multiple IEEE test systems. [ABSTRACT FROM AUTHOR]

This paper considers distribution systems with a high penetration of distributed, renewable generation and addresses the problem of incorporating the associated uncertainty into the optimal operation of these networks. Joint chance constraints, which satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints, leading to a chance-constrained optimal power flow problem. Departing from the computationally heavy scenario-based approaches or approximations that transform the joint constraint into conservative deterministic constraints; this paper develops a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and accurately and efficiently approximates bounds on the joint chance constraint iteratively. In particular, the proposed framework improves upon the classic methods based on the union bound (or Boole’s inequality) by generating a much less conservative set of single chance constraints that also guarantees the satisfaction of the original joint constraint. The proposed framework is evaluated numerically using the IEEE 37-node test feeder, focusing on the problem of voltage regulation in distribution grids. [ABSTRACT FROM AUTHOR]

We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The framework explicitly combines multi-stage feedback policies with any forecasting method and historical forecast error data. The objective is to determine power scheduling policies for controllable devices in a power network to balance operational cost and conditional value-at-risk of device and network constraint violations. These decisions include both nominal power schedules and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming that the uncertainties across the networks follow prescribed probability distributions, we consider ambiguity sets of distributions centered around a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real unknown data-generating distribution, we formulate a multi-stage distributionally robust OPF problem to compute control policies that are robust to both forecast errors and sampling errors inherent in the dataset. Two specific data-based distributionally robust stochastic OPF problems are proposed for distribution networks and transmission systems. [ABSTRACT FROM AUTHOR]

This is the second part of a two-part paper on data-based distributionally robust stochastic optimal power flow. The general problem formulation and methodology have been presented in Part I (Y. Guo, K. Baker, E. Dall’Anese, Z. Hu, and T.H. Summers, “Data-based distributionally robust stochastic optimal power flow—Part I: Methodologies,” IEEE Trans. Power Syst., 2018.). Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance. On the distribution side, the method mitigates overvoltages due to high photovoltaic penetration using local energy storage devices. On the transmission side, the method reduces $N-1$ security line flow constraint risks due to high wind penetration using reserve policies for controllable generators. In both cases, the data-based distributionally robust model-predictive control algorithm explicitly utilizes forecast error training datasets, which can be updated online. The numerical results illustrate inherent tradeoffs between the operational costs, risks of constraints violations, and out-of-sample performance, offering systematic techniques for system operators to balance these objectives. [ABSTRACT FROM AUTHOR]

This paper examines distribution systems that have a high integration level of distributed energy resources (DERs), and addresses the design of local control methods for real-time voltage regulation. Particularly, the paper focuses on proportional control strategies wherein the active and reactive power output of DERs are adjusted in response to (and proportionally to) local changes in voltage levels. The design of the voltage-active power and voltage-reactive power characteristics leverages suitable linear approximations of the ac power flow equations and is network-cognizant; that is, the coefficients of the controllers embed information on the location of the DERs and forecasted noncontrollable loads/injections and, consequently, on the effect of DERs power adjustments on the overall voltage profile. A robust approach is pursued to cope with uncertainty in the forecasted noncontrollable loads/power injections. The stability of the proposed local controllers is analytically assessed and numerically corroborated. [ABSTRACT FROM AUTHOR]

This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers. [ABSTRACT FROM PUBLISHER]

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast of the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage. [ABSTRACT FROM PUBLISHER]

In electric power systems, multiple entities are responsible for ensuring an economic and reliable way of delivering power from producers to consumers. With the increase of variable renewable generation it is becoming increasingly important to take advantage of the individual entities’ (and their areas’) capabilities for balancing variability. Hence, in this paper, we employ and extend the approximate Newton directions method to optimally coordinate control areas leveraging storage available in one area to balance variable resources in another area with only minimal information exchange among the areas. The problem to be decomposed is a model predictive control problem including generation constraints, energy storage constraints, and AC power flow constraints. Singularity issues encountered when formulating the respective Newton–Raphson steps due to intertemporal constraints are addressed and extensions to the original decomposition method are proposed to improve the convergence rate and required communication of the method. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously. [ABSTRACT FROM AUTHOR]

Recently, given the increased integration of renewables and growing uncertainty in demand, the wholesale market price has become highly volatile. Energy communities connected to the main electricity grid may be exposed to this increasing price volatility. Additionally, they may also be exposed to local network congestions, resulting in price spikes. Motivated by this problem, in this paper, we present a coordination mechanism between entities at the distribution grid to reduce price volatility. The mechanism relies on the concept of duality theory in mathematical programming through which explicit constraints can be imposed on the local electricity price. Constraining the dual variable related to price enables the quantification of the demand-side flexibility required to guarantee a certain price limit. We illustrate our approach with a case study of a congested distribution grid and an energy storage system as the source of the required demand-side flexibility. Through detailed simulations, we determine the optimal size and operation of the storage system required to constrain prices. An economic evaluation of the case study shows that the business case for providing the contracted flexibility with the storage system depends strongly on the chosen price limit. [ABSTRACT FROM AUTHOR]