Affinely Adjustable Robust Volt/VAr Control Without Centralized Computations

Abstract

This paper proposes a completely non-centralized Volt/VAr control (VVC) algorithm for active distribution networks which are faced with voltage magnitude violations due to the high penetration of solar photovoltaics (PVs). The proposed VVC algorithm employs a two-stage architecture where the settings of the classical voltage control devices (VCDs) are decided in the first stage through a distributed optimization engine powered by the alternating direction method of multipliers (ADMM). In contrast, the PV smart inverters are instructed in the second stage through linear Q(P) decision rules -which are computed in a decentralized manner by leveraging robust optimization theory. The key to this non-centralized VVC routine is a proposed network partition methodology (NPM) which uses an electrical distance metric based on node Q − |V | 2 sensitivities for computing an intermediate reduced graph of the network, which is subsequently divided into the final partitions using the spectral clustering technique. As a result, the final network partitions are connected, stable, close in cardinality, contain at least one PV inverter for zonal reactive power support, and are sufficiently decoupled from each other. Numerical results on the UKGDS-95 and IEEE-123 bus systems show that the non-centralized solutions match closely with the centralized robust VVC schemes, thereby significantly reducing the voltage violations compared to the traditional deterministic VVC routines.

Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

“…It is easy to prove the convexity of ICNN: ① in the linear X W [1] , b [1] W [2] , b [2] W [3] , b [3] Z [1] A [1] Z [2] A [2] Z [3] A [3] Z…”

“…T HE problem of voltage and reactive power in local grid varying wildly and randomly brought by the uncertainty of distributed energy spawns the upgrade of volt-var control (VVC) to be more accurate and faster [1] - [3]. Due to the nonlinear and nonconvex nature of power flow (PF) constraints in the optimization model, VVC is essentially a kind of non-convex and nonlinear programming problem, which can easily fall into local optimum with no pretreatment [4], [5].…”

Ultra-short-term dynamic prediction of multiple types of energy sources based on two-stage transfer learning

“…Since the ultra-short-term load data are easily affected by many factors, the EMD algorithm is first used to decompose the normalized original load data into several IMFs and residuals, and then the kmeans algorithm is used to cluster the decomposed IMFs and residuals. [10] According to the clustering results, the components within the same group are superimposed to form a new time series, which is then passed into the neural network for prediction. The structure of the proposed algorithm is shown in Figure 3.…”

Reactive Power Potential of Converter-Connected Renewables Using Convex Power Flow Optimization

“…The reactive power potential of a converter depends on two factors; the rated current 𝐼 𝑚𝑎𝑥 and, the minimum and maximum rated voltage for the converter. Most research works focus on the maximum current constraint on the converters and fail to account for the converter voltage constraints, thus leaving the converter reactive power capability model incomplete [13,16,[24][25][26][27][28][29][30][31][32][33][34][35].…”