Towards Smart Planning: From Data Readiness to DER Analytics

Utilities are becoming more data-driven by developing solutions to utilise the existing data sources such as SCADA, GIS, ADMS, AMI, OMS, CIS, etc., but also dealing with the acquired data from the installation of new RTUs and additional sensors at the primary & secondary substations. The LV network has traditionally been designed as a passive network i.e. to accommodate the maximum load or demand required by customers with consideration to the increase of demand in the longer term. For this reason, the network has been designed with minimum or no observability and controllability in place. The increase proliferation of Distributed Energy Resources (DER) has changed the dynamics of the LV network. Bi-directional power flows and the increase installation of EVs will cause voltage violations, line and transformer overloads on the network. If the grid is not managed smartly, it will lead to increased network losses, and shorter life expectancy of the most critical (hence expensive) assets e.g. transformers. Moreover, the increase of DERs on the distribution network will also lead to tap changers (which are mechanical components) of primary transformers having to work much harder to regulate the voltage. This in turn will significantly reduce their (i.e. tap changers) life expectancy. Implementation of smart grid technology will offer solutions to manage the challenges facing the distribution grid operators. These solutions and technologies will improve the following:

-       Network observability at the LV by installing smart meters and installation of RTUs at the secondary substations

-       LV network controllability by introducing battery storage and demand response

-       Network efficiencies by implementing Volt-var controls and optimisation

-       Use of smart inverter technology to control and optimise the voltage profile with less reliance on tap changers in primary substations 

-       Network resiliency and reliability by implementing technologies such as microgrids and Fault Location Isolation and Supply Restoration (FLISR)          

For utilities to be able to decide on the ‘suitable’ technology to resolve their challenges e.g. demand response, smart charging, OLTC, ANM, etc., they will need to improve network visibility by utilising the utility data, and implementing advanced analytics to get insights on the network which wouldn't normally be visible from traditional systems such as SCADA. The value chain of the system is shown in the figure below:

We realize that not all utilities are ready with the roll-out of smart meters and the subsequent utilisation of the smart meter data to improve visibility on the distribution network, with planning still being a challenge for many utilities. As such, a solution to connect to the available data sources such as PI historian to cleanse the data and prepare it for DER planning on the network will offer a smart planning solution. The system will be built on the basis that once smart meter data are obtained and ingested, more advanced analytics for the utility can be implemented.

A basic architecture of the solution which can be on-premise or cloud-based, is shown in the figure below:

The process of building the solution would consist of the following steps:

-       Connection and data integration: This is the connection of the solution to the available data sources mainly the historian such as PI, Asset management system, Geographic information system (GIS), LV SCADA system, DER and connection data bases. The purpose of this phase is to ensure that the data ingested are of good quality, available, effective, and will represent the data source of truth for the consequent data planning and analytics

-       Data Cleansing, Preparation and Analytics: This will be the phase where we process the available measurement data stored in the historian. Sources of the data would typically be the SCADA system for HV and LV, and other measuring devices. Data will be cleansed (e.g. improve missing values, zero values, spikes, etc.), and prepared (e.g. find min and max of load profile values for the past 2-5 years for secondary substations or feeders, remove effects of DG on the load profiles to represent demand only) to feed into further planning activities to study the effect of DER on the MV and LV networks.

-       DER Analytics: this could be used to perform power flow calculations based on the network topology, and connectivity model with DERs in the network. Estimation of hosting capacity, DER connection assessments, grid stress using probabilistic approach and time-series network simulation could all be performed in the same solution  

*In grey will need smart meter data

 The benefits we offer to utilities by implementing our integrated data readiness and DER Analytics approach are:

-       One solution provided by one company for the data connection, integration, data cleansing, preparation and network simulation

-       Utilities are able to use an easy and flexible platform instead of using custom-made tools built in-house using Microsoft Excel. Such tools may lead to loss of valuable time, effort and flexibility in analysing the data and doing more advanced DER analytics

-       A solution offering basic analytics used by utilities who have not progressed with the roll out of their smart meters. The same solution could be used in the future to integrate smart meter data once rolled out to perform more advanced analytics and smart planning.

-       Efficient, effective and easy to use planning tool to study and investigate the DER effect on the network, when compared to other traditional planning tools existing in the market

-       The solution could be offered as cloud-based, hence offered through a SaaS model with a yearly/monthly subscription

-       It is designed to enable customer and investor engagement

Utilities will need to become more data driven for better reliability, resiliency, and efficiency. Planning work in the MV and LV networks will benefit from an integrated approach to the data connection, cleansing, preparation and basic analytics when combined with DER Analytics for network simulation. With the increase penetration of DERs in the MV and LV networks, efficiencies are required to perform planning activities such as: connection requests evaluation, hosting capacity estimation, grid stress due to penetration of EVs and PVs, as well as time-series simulation. This integrated approach will offer more successful project delivery in time, cost, and quality due to having one responsible partner dealing with the data readiness and simulation works. It will also offer planners and other internal stakeholders to perform some data analytics on the existing measured data on the MV and LV before the roll-out of smart meters. The same platform could be used in the future to perform more advanced analytics with smart DER planning using smart meter data.