Update README.md

README.md

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 # SolarSys: Scalable Hierarchical Coordination for Distributed Solar Energy
 ## Abstract
 
-To address these limitations, we design SolarSys, a hierarchical coordination system for peer-to-peer energy trading in distributed
-solar and storage communities.
-SolarSys integrates local power sensing from smart meters and inverters with edge computation and distributed coordination to manage energy usage across large residential networks.
-
-The system is organized hierarchically to support decision making at both the household and cluster levels. 
-
-Each household node measures its generation, demand, and battery state, and makes local energy management decisions using a policy trained with Multi-Agent Proximal Policy Optimization to improve local solar usage and maintain cluster-level energy balance.
-
-Across clusters, SolarSys leverages a mean-field multi-agent reinforcement learning approach that updates high-level decisions using aggregated cluster conditions.
+Distributed solar energy resources (DSERs) in residential power systems are rapidly increasing due to declining photovoltaic module prices. This increase has led to the development of virtual power plants (VPPs) that coordinate local generation to support reliable and efficient grid operation. However, many existing VPPs rely on centralized control, where a central operator aggregates household-level information and determines trading outcomes, preventing individual users from independently and concurrently trading surplus energy. 
+As a result, researchers have explored decentralized coordination, where individual households trade surplus solar energy directly with others to improve utilization and fairness within residential networks. However, such approaches perform well only in small deployments and become inefficient at scale as the number of participating households increases.
 
-This design allows clusters to adjust their decisions so the overall system remains consistent with practical grid constraints.
-
-In addition, we deploy SolarSys on a Raspberry Pi and find that the learned policies can run efficiently on low-power edge hardware.
-
-We evaluate SolarSys using real smart meter data from seven residential communities. 
+To address these limitations, we design SolarSys, a hierarchical coordination system for peer-to-peer energy trading in distributed
+solar and storage communities. SolarSys integrates local power sensing from smart meters and inverters with edge computation and distributed coordination to manage energy usage across large residential networks. The system is organized hierarchically to support decision making at both the household and cluster levels. Each household node measures its generation, demand, and battery state, and makes local energy management decisions using a policy trained with Multi-Agent Proximal Policy Optimization to improve local solar usage and maintain cluster-level energy balance.
 
-We find that SolarSys reduces the energy drawn from the grid by 27.48 ± 0.42\%, increases daytime solar utilization to 82.76 ± 5.11\%, and improves fairness to 0.773 (Jain’s Index).
+Across clusters, SolarSys leverages a mean-field multi-agent reinforcement learning approach that updates high-level decisions using aggregated cluster conditions. This design allows clusters to adjust their decisions so the overall system remains consistent with practical grid constraints. In addition, we deploy SolarSys on a Raspberry Pi and find that the learned policies can run efficiently on low-power edge hardware.
 
-The results show that SolarSys enables efficient, fair, and scalable peer-to-peer energy trading in large-scale virtual power plant deployments.
+We evaluate SolarSys using real smart meter data from seven residential communities. We find that SolarSys reduces the energy drawn from the grid by 27.48 ± 0.42\%, increases daytime solar utilization to 82.76 ± 5.11\%, and improves fairness to 0.773 (Jain’s Index). The results show that SolarSys enables efficient, fair, and scalable peer-to-peer energy trading in large-scale virtual power plant deployments.
 
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