Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration

Abstract

In this paper, a locational marginal pricing algorithm is proposed to control the voltage in unbalanced distribution grids. The increasing amount of photovoltaic (PV) generation installed in the grid may cause the voltage to rise to unacceptable levels during periods of low consumption. With locational prices, the distribution system operator can steer the reactive power consumption and active power curtailment of PV panels to guarantee a safe network operation. Flexible loads also respond to these prices. A distributed gradient algorithm automatically defines the locational prices that avoid voltage problems. Using these locational prices results in a minimum cost for the distribution operator to control the voltage. Locational prices can differ between the three phases in unbalanced grids. This is caused by a higher consumption or production in one of the phases compared to the other phases and provides the opportunity for arbitrage, where power is transferred from a phase with a low price to a phase with a high price. The effect of arbitrage is analyzed. The proposed algorithm is applied to an existing three-phase four-wire radial grid. Several simulations with realistic data are performed.

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

“…The pollutant emissions of the distribution system mainly consist of two parts, namely, the pollutant emissions from thermal power units and the pollutant emissions from of DG units. An environmental cost index is given as follows: Q n,i pV n,i`P N n,i qs (6) where 1/(1+η) b represents the present value of the annual cost considering discount rate η in the bth year; T is the total number of DG planning years; S DG,n and pf DG,n are the installed capacity and power factor of the nth DG; P sub,n is the power of nth thermal power unit; T DG,n and T sub,n are the equivalent generation hours of DG and thermal power generation in one year; N DG and N sub are the total number of DGs and substations in the distribution network; Q n,i is the amount of the ith pollutant emissions from the nth generator per active power (kg/kWh); and V n,i and PN n,i are the environmental treatment cost and penalty fee of the ith pollutant ($/kg), respectively [21]; K is the types of pollutant.…”

“…Reasonable application of DGs can bring many advantages, such as voltage profile improvement and pollutant emission reduction [3][4][5]. However, inappropriate allocation of DGs may also lead to voltage fluctuations and system instability due to the uncertain nature of renewable resources [6,7]. It is crucial to develop proper models and methodologies to identify the optimal allocation of DGs, the aim of which is to determine the best types, locations and sizes of DGs taking into account system uncertainties.…”

A Closed-Loop Control Strategy for Air Conditioning Loads to Participate in Demand Response

“…1 c and 2 c are the acceleration factors and their general values are between (0, 2). 1 r , 2 r are random functions, scaling values range from 0 to 1.…”

“…The particle swarm optimization algorithm is applied to tune parameters for the PID controller. The parameters of the PSO algorithm are set as follows: population size m = 100, dimension D = 3, the 3 parameters Kp, Ki and Kd, to be optimized are in the range of 0-300, the inertia weight w = 0.6, the maximum number of iterations t = 200, acceleration coefficients 1 The IEAT index is chosen as the fitness function, with the minimum fitness value being 0.1. The particle velocity is between [−1, 1].…”

“…It is considered a critical application in smart grids. With the Advanced Metering Infrastructure (AMI), the power usage of different appliances can be adjusted either directly, e.g., through incentive-based programs (IBP), such as operational parameters/states changing requested by grid operators, direct load control, and interruptible loads; or indirectly, e.g., through price-based programs (PBP) [1], such as real-time pricing, time of use pricing, and critical peak pricing [2,3]. By smoothing out the system power demand over time, DR is capable of providing peak shaving, load shifting and ancillary services to achieve system reliability and stability.…”