Load optimization in distribution grids is a software-based operating solution for low- and medium-voltage networks that helps utilities manage growing pressure from distributed energy resources, electric vehicles, heat pumps and more variable demand. It shifts distribution-grid operation away from a model based on limited real-time visibility, conservative planning margins and network reinforcement as the default response to local constraints.  

That earlier approach was suited to more predictable one-way power flows and largely passive consumers, but it is less effective in networks with rapidly changing load and voltage conditions driven by distributed and flexible assets. Load optimization software responds by combining grid visibility, power-flow analytics, short-term forecasting and control of flexible assets to show where the network is approaching technical limits and to act before those limits are breached.  

In practice, the software ingests data from grid sensors, smart meters, distributed energy resource management systems and operational platforms to detect congestion risks, voltage issues and capacity constraints across feeders and substations. It then uses tools such as demand response, distributed energy resource coordination and dynamic operating limits to shift or shape loads, improve power quality and keep more demand and generation within the safe operating range of the existing network.  

This matters because distribution grids are under increasing strain, while conventional reinforcement remains capital-intensive and slow to deliver. By improving the utilization of existing assets and enabling more targeted flexibility, load optimization software can defer avoidable network upgrades, support faster integration of distributed resources and strengthen reliability in a more electrified and decentralized power system. 

Main Outcomes of the Solution 

• Reliability & Flexibility: Improves distribution-grid reliability by giving operators real-time visibility of emerging congestion, voltage deviations and local capacity constraints, then using forecasting and control of flexible assets to respond before these develop into service or power-quality problems. It also increases system flexibility by coordinating distributed energy resources, demand response and dynamic operating limits in networks with more variable two-way power flows.  
• Affordability: Lowers system costs by improving utilization of existing grid assets and reducing avoidable reinforcement. By managing peak demand and local bottlenecks through software and flexibility, utilities can defer capital-intensive upgrades and operate the network more efficiently.  
• Sustainability: Supports faster integration of distributed renewable generation and electrified end uses by creating more usable hosting capacity on existing networks, reducing curtailment risk and enabling cleaner load growth without waiting solely for physical grid expansion. 

Key implementation and scaling factors 

1. Deploy granular visibility at feeder and substation level through network monitoring, smart meters and distributed energy resource telemetry so operators can detect local constraints early enough to intervene effectively. 
2. Scale digital systems that can aggregate and remotely coordinate large numbers of distributed devices through interoperable smart meters, management systems and distributed energy resource platforms. 
3. Align market rules and incentives so distributed resources can participate in flexibility services and customer behavior can support grid needs. 
4. Build planning, investment and operational capability so utilities can embed these tools into routine grid management rather than treating them as stand-alone pilots. 
5. Establish secure digital architecture and data governance, as greater connectivity and automation increase cyber and data-management risks. 

Case study 

Plexigrid (2022), Sweden 

In 2022, Plexigrid launched a partnership with distribution system operator Falbygdens Energi Nät in Falköping, Sweden, to use digital grid planning, forecasting and flexibility management to address local connection constraints. The project focused on improving visibility of the distribution network using data from smart meters and other grid data, helping identify where customers could connect earlier through flexibility and better use of existing capacity.  

A subsequent Plexigrid case study reported that the approach was used to unlock 20 MW of new photovoltaic capacity, integrated with a utility-scale battery at medium-voltage level and supported by a flexible connection agreement.  

The case demonstrates how active distribution-grid management can increase renewable hosting capacity and support new connections without relying solely on conventional network reinforcement. By combining grid visibility, forecasting and flexible connection arrangements, the approach helps utilities make better use of existing infrastructure while maintaining safe operating limits on the local network. 

References: 

1. The Value of Demand Flexibility – Analysis - IEA 
2. Smart grids - IEA 
3. Falbygden's Case Study: Flexibility in Practice - Plexigrid 
4. Falbygdens Energi Nät and Plexigrid cut the waiting time to connect to the grid - Plexigrid 
5. Flexibility in Practice: Increasing Renewable Hosting Capacity - Plexigrid