"SIU – Ameren Illinois DER Project" Development of analytical software to evaluate Distributed Energy Resources (DER) including Energy Storage.

- Project Definition:

Project Title: Development of analytical software to evaluate Distributed Energy Resources (DER) including Energy Storage and Inverter.

SIU – Ameren DER Project

Research Project

Date: 2018 - 2020

• Software Development
• Project Funded by Ameren Electic Company

Published Papers:

1- Security/stability-based Pareto optimal solution for distribution networks planning implementing NSGAII/FDMT

2- Employing load and irradiance profiles for the allocation of PV arrays with inverter reactive power and battery storage in distribution networks–A fast comprehensive QSTS technique

3- Probabilistic Quasi-Static Time Series Simulation for Distribution Network Planning Considering Multiple Uncertainties of PV and Load in the Presence of BESS

Problem Statement

Increased penetration of Distributed Energy Resources will create challenges to utilities electric distribution systems. These challenges include the following issues:

·????????Voltage & Frequency Regulation of electric distribution circuits through the use of inverter outputs including inverters from Energy Storage (Battery) installations.

·????????Improving power losses through the use of inverters to manage reactive power.

·????????Determination of Hosting Capacity limits on individual electric distribution circuits.

·????????Improving electric distribution circuit operation through the inclusion of Energy Management concepts focused on Demand forecasting, Renewable Resource (Wind/PV) forecasting, and proper charging/discharging of Energy Storage battery systems.

Utilities need to address the above challenges as they plan and design the electric distribution circuits of the future. Development of an analytical model that can be used to evaluate electric distribution circuits' responses to higher penetration of DER assets will allow the utility to understand how these DER assets can improve electric distribution circuit performance.?

Questions to be answered by the research initiative

1.??????How should the analytical model assess the following issues:

a.??????Customer demand – Load forecasting

b.??????Renewable resource control/coordination including what methods provide the best integrated control of these resources

c.??????Power losses across the electric distribution circuit.

d.??????Voltage regulation across the electric distribution circuit.

e.??????Integration of Energy Storage systems to manage the intermittency of Renewable resources.

2.??????How can the analytical model assess DER resources utilization to manage Volt Var Optimization across an electric distribution circuit?

3.??????How can the analytical model assess Energy Management functions/scheduling to improve electric distribution circuit performance?

4.??????How can the analytical model incorporate weather data to forecast renewable resource performance to improve electric distribution circuit performance?

5.??????What electric distribution circuit data is necessary to inform the analytical model of how to improve electric distribution circuit performance.

Deliverables

?An analytical model that will allow engineers to assess how Distributed Energy Resources can be utilized to improve electric distribution circuit performance under increased penetration of renewable energy resources, including energy storage systems.

Research Approach

For given AIC distribution feeder(s), review the design, configuration, and historical feeder performance. Review the list of challenges that DER resources, including energy storage systems, present to the electric distribution circuit infrastructure. Work with Ameren Illinois Engineer(s) to review the currently available solutions that are available to assess the list of challenges. Develop an analytical model that will allow Ameren Illinois engineers to perform assessments to address the stated challenges.

- Project Implementation:

* Introduction:

This developed software demonstrates the effective use of advanced computational genetic techniques for the optimum placement of large-scale PV arrays on the distribution grid. The software also reflects the impact of PV arrays on the distribution system and their effectiveness in reducing system losses and maintaining a reasonable voltage profile. The concept of voltage stability index is introduced. The software also demonstrates the use of a battery bank combined with a PV array for achieving load smoothing. The effort was funded in great part by Ameren. Ameren also provided technical expertise and data that helped to fine-tune the software.

-- This software includes the following parts:

1- Creating Network Matrices

2- Network Reduction

3- Load Flow Calculation

4- Battery Calculation, Peak Shaving

5- Reactive Power Compensation/ Voltage Optimization

6- Sitting and Sizing of PV by NSGA-II

7- Optimization: Non-dominated Sorting Genetic Algorithm & Fuzzy Decision Making-Tool (FDMT)

8- Power System Security/Stability Analysis

9- Annual Load Flow (8760-hr)

10- Probabilistic Quasi-Static Time Series Simulation (8760-hr Solution)

11- Multiple Uncertainties of PV and Load in the Presence of BESS

* Some Snapshots from Software:

• Main Page:

• Data Entry (Access):

A detail of the profile used in the study, (a) normalized substation demand profile, (b) Load profile vs. an hour and day of the week.

detail of the normalized hourly irradiance used in the sty, (a) solar irradiance for one year, (b) PV Profile in the specific hour of a year, (c) PV profile vs. an hour and day of the week.

• Network Reduction:

Technical Part:

??????The problem: The distribution network, which may comprise one or multiple feeders, typically contains a large number of nodes and branches, making the numerical analysis (unnecessarily) slow and cumbersome.

A suitable reduced-order model is computed from the initial data.

??????The resulting network retains significantly fewer nodes and branches from the original while also retaining the effects of the reduced portions and their load.

• Load Flow Calculation

Traditional load flow solution techniques such as Newton Raphson and Gauss-Seidel are inefficient in the analysis of distribution systems with radial structure and due to high ratio may cause numerical problems for the power flow algorithm. Therefore, in this research, the direct load flow method is implemented based on the bus-injection to branch-current incidence matrix (BIBC) and the branch-current to bus-voltage impedance matrix (BCBV).

-Typical DN with load/PV Profiles and Uncertainties

• Optimization

• Load Flow, PV/Battey Calculations

• 8760 Load Flow

- PV/Battery output

- Optimization Process

-Effect of voltage regulation from the PV array

-Distribution of PV array size to system nodes: (a) 8760 solutions; (b) peak load solutions.

-Example of peak load shaving

-Example: Peak load shaving with a 1938-kW array with 3 batteries at node 73, (a) reduction in peak load vs. number of days in a year, (b) frequency of reduction.

-Example: Pareto Front

-Example: DN Voltage Profile

-?Results by ID Number/Weighting Factor

• Example --Log File: