This paper studies the probabilistic hosting capacity analysis (PHCA) problem in distribution networks considering uncertainties from distributed energy resources (DERs) and residential loads. PHCA aims to compute the hosting capacity, which is defined as the maximal level of DERs that can be securely integrated into a distribution network while satisfying operational constraints with high probability. We formulate PHCA as a chance-constrained optimization problem, and model the uncertainties from DERs and loads using historical data. Due to non-convexities and a substantial number of historical scenarios being used, PHCA is often formulated as large-scale nonlinear optimization problem, thus computationally intractable to solve. To address the core computational challenges, we propose a fast and extensible framework to solve PHCA based on Bayesian Optimization (BayesOpt). Comparing with state-of-the-art algorithms such as interior point and active set, numerical results show that the proposed BayesOpt approach is able to find better solutions (25% higher hosting capacity) with 70% savings in computation time on average.
This chapter proposes an evolved concept of "hosting capacity" using the term of "feasible region". Through converting the grid model into a more compact one, "hosting capacity region" not only is promising to further exploit the grid potential for power delivery, but also benefits grid operation feasibility investigation with concise formulas. The hosting capacity region assessment schemes are exploited as well. Facing the derived hosting capacity, originally complicated energy storage optimization problems can be represented algebraically, which is more efficient and friendly for computer pr…
This paper revisits the generation hosting capacity (HC) calculation approach to account for grid operational flexibility--the ability to reconfigure the system safely. In essence, the generation hosting capacity is determined against the set of limiting factors--voltage, thermal (conductor loading), reverse flow (at the feeder head, station transformer, or substation), and change in the voltage (due to sudden change in generation output)). Not that long ago, California Investor-Owned Utilities (IOUs) added a new criterion that does not allow reverse flow at the supervisory control and data ac…
This paper presents a new method for assessing the amount of photovoltaics that can be accommodated on a distribution feeder before disrupting the normal operational conditions, commonly referred to hosting capacity. An optimal-capacity-based (OCB) PV allocation method is proposed to evaluate PV hosting capacity. We fist use load allocation method to allocate realistic load profiles to each load node down to each household. Instead of randomly assigning the installed capacity of PV to each household, the optimal size of PV for each house is first calculated based on the annual load profiles. D…
The fast depletion of conventional energy sources due to increased energy demands and environmental concern has motivated power utilities to integrate more renewable energy sources into their power systems. Due to the intermittent nature and low or non-existent inertial response of these sources, a high penetration of RESs can lead to various issues in the operation of power systems such as oscillations in power system voltage and frequency, increased harmonic distortion, failure of protective equipment, overloading of transformers and feeders, and increased line losses. Such problems arise wh…
Smart grids are envisioned to accommodate high penetration of distributed photovoltaic (PV) generation, which may cause adverse grid impacts in terms of voltage violations. Therefore, PV Hosting capacity (HC) is being used as a planning tool to determine the maximum PV installation capacity that causes the first voltage violation and above which would require infrastructure upgrades. Traditional methods of HC analysis are computationally complex as they are based on iterative load flow algorithms that require investigation of a large number of scenarios for accurate assessment of PV impacts. T…
Power systems face increasing challenges on reliable operations due to the widespread distributed generators (DGs), e.g., rooftop PV system in the low voltage (LV) distribution grids. Characterizing the hosting capacity (HC) is vital for assessing the total amount of distributed generations that a grid can hold before upgrading. For analyzing HC, some methods conduct extensive simulations, lacking theoretical guarantees and can time-consuming. Therefore, there are also methods employing optimization over all necessary operation constraints. But, the complexity and inherent non-convexity lead t…
Solar hosting capacity analysis (HCA) assesses the ability of a distribution network to host distributed solar generation without seriously violating distribution network constraints. In this paper, we consider risk-sensitive HCA that limits the risk of network constraint violations with a collection of scenarios of solar irradiance and nodal power demands, where risk is modeled via the conditional value at risk (CVaR) measure. First, we consider the question of maximizing aggregate installed solar capacities, subject to risk constraints and solve it as a second-order cone program (SOCP) with …
This paper presents an analysis of the value that can be realized by medium-voltage back-to-back (MVB2B) converters in terms of increased utilization rate of distributed energy resource (DER) and the improvement in operational conditions. A systematic, transferrable, and scalable methodology has been designed to analyze and quantify the increased DER value from three perspectives: 1) curtailment reduction of the DER generation, 2) size reduction of the energy storage needed to otherwise realize DER hosting levels, and 3) hosting capacity improvement of DER compared to base distribution circuit…
This paper proposes a hierarchical Bayesian model for probabilistic estimation of the electric vehicle battery capacity fade. Since the battery aging factors such as temperature, current, and state of charge are not fixed, and they change in different times, locations and by the different users, deterministic models with constant parameters cannot accurately evaluate the battery capacity fade. Therefore, a probabilistic presentation of the capacity fade including uncertainties of the measurements or observations of the variables can be a proper solution. We have developed a hierarchical Bayesi…
This paper proposes a strategy to size a grid-connected photovoltaic plant coupled with a battery energy storage device within the \textit{capacity firming} specifications of the French Energy Regulatory Commission. In this context, the sizing problem is challenging due to the two-phase engagement control with a day-ahead nomination and an intraday control to minimize deviations from the planning. The two-phase engagement control is modeled with deterministic and stochastic approaches. The optimization problems are formulated as mixed-integer quadratic problems, using a Gaussian copula methodo…
This work presents an optimization framework to aggregate the power and energy flexibilities in an interconnected power distribution systems. The aggregation framework is used to compute the day-ahead dispatch plans of multiple and interconnected distribution grids operating at different voltage levels. Specifically, the proposed framework optimizes the dispatch plan of an upstream medium voltage (MV) grid accounting for the flexibility offered by downstream low voltage (LV) grids and the knowledge of the uncertainties of the stochastic resources. The framework considers grid, i.e., operationa…
This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. The core contribution is to propose a probabilistic forecast-driven strategy, modeled as a min-max-min robust optimization problem with recourse. It is solved using a Benders-dual cutting plane algorithm and a column and constraints generation algorithm in a tractable manner. A dynamic risk-averse parameters selection strategy based on th…
