Design of a Multi-Agent System for Distributed Voltage Regulation Dimitrios Athanasiadis, Minjiang Chen, Badr Al Faiya, Stephen McArthur, Ivana Kockar University of Strathclyde, Glasgow, UK Haowei Lu, Francisco de Leon New York University, New York, USA ETP October 10 2017

Agenda Research Introduction Voltage Control Based on DGs Analysis of ε Decomposition Design of MAS for Distributed Voltage Control Agent Types & Functions Agent Simulation Platform MAS Implementation & Case Study Conclusion & Future Work

Research Introduction Distributed power technologies are more widely available, smaller, more efficient and less costly than they were a decade ago. However, the rise of Distributed Generation (DG) is encountered with various network control challenges, such as: voltage control. The distributed control method with less information and communications is needed as network control creates a heavy computational burden on large systems. Intelligent Multi-Agent System (MAS) is one of most suitable technologies for implementing such distributed system control with flexibility and extensibility. But the rise of distributed power is also

Voltage Control Based on DGs An optimal voltage regulation [1] is proposed by using the ε decomposition to group large network into several sub-networks. After applied ε decomposition value, the strong couplings between the DGs are kept and the weak couplings are abandoned. DGs and their influence on voltage and grouping can be obtained with the ε decomposition. Zone of influence of DGs The proposed voltage regulation is suitable for both power factor control and unity power factor control mode. [1] Y. Li, D. Czarkowski, and F. De León. "Optimal distributed voltage regulation for secondary networks with DGs." IEEE Transactions on Smart Grid, vol. 3, no. 2, pp. 959-967, Jun. 2012.

Analysis of ε Decomposition For different ε decomposition value, the number of DG groups, percentage of covered nodes and a maximum number of covered nodes in one group are various. For different ε decomposition value, there are different successful control rate for voltage violation and power loss.

Design of MAS for Distributed Intelligent Control A distributed intelligent control based MAS framework was proposed for plug-and-play application. The proposed MAS for distributed voltage control contains four types of agent: ε decomposition agent; Linear Programming Solver (LPS) agent; Network Violation Detection (NVD) agent and DG agent. After the large network is divided into a number of sub-networks, each sub-network has one LPS agent, one NVD agent and one or more DG agents. Each agent only needs to communicate with other agent that are in the same sub-network. ε decomposition agent is the one to generate or kill the LPS and NVD agent based on the different ɛ value. The number of LPS agents and NVD agents are varies based on the different ε decomposition value.

Agent Functions ε Decomposition Agent: Has knowledge of ɛ value so that it will update ɛ value if there is a network changing (e.g. adding a new DG) or failing to solve the voltage violation. DG Agent: Takes action to control the DG after receiving a command from LPS agent. It has ability to update its voltage influence range according to different ɛ value. LPS Agent: Solves the voltage violation within sub-network with integrated linear programming algorithm. Once solution is determined, LPS agent sends control value to related DG agents by messages. If LPS cannot find solution, it will send a message to the decomposition agent about failing to find solution. NVD Agent: Monitors the status of its sub-network and identifies network violation (voltage or thermal). NVD agent has knowledge of network operational limit (e.g. voltage: 0.95-1.05 p.u.). Once a violation is detected, it communicates with LPS agent with violation nodes.

Agent Simulation Platform The proposed MAS is being implemented within the simulation of agent societies framework named Presage. Presage is a Java based programming environment that provides improved autonomy and agent communication capability that contains abstract classes and interfaces for user to extend. Presage2 is the second version of Presage extends the original platform by adding the support for declarative rule specifications and increasing modularity. Presage2 defines its own agent communication language and agent collaborate each other by agent communications within the agent message transport system.

MAS Implementation & Case Study Step1: Initializing the system with an ɛ decomposition value. Step2: ɛ decomposition agent determines the initial number of LPS agents and NVD agents and informs LPS agents and NVD agents about their subnetwork agents with names and addresses. Step3: NVD agent starts to check for voltage violation and informs LPS agent if voltage violation occurs. LPS agent determines the solution once receives violation message and sends control actions to related DG agents. Step4: If LPS agent cannot find solution it sends a message to ɛ decomposition agent to change ɛ value. If the LPS agent finds a solution and it cannot solve the violation, the NVD agent will send a message to ɛ decomposition agent to change another ɛ decomposition agent. When ɛ decomposition agent determines a new ɛ value, it will regenerates the sub-networks and create new LPS agents and NVD agents and move to Step 3 to continue to solve the issue. The first violation occurred in subnetwork 1 and LPS agent 1 found a control solution after received violation message. After that, LPS agent 1 sends control value to the DG agent 1. The second violation is detected in the subnetwork 5. However, the LPS agent 5 did not find solution and therefore the ɛ decomposition agent selects a new value. The second MAS Voltage Control Implementation Flow Chart Distributed Network for Case Study

Agent Communications

Case Study With second ɛ decomposition value, the secondary distribution network is grouped into a new four subnetworks with different DG grouping.

Case Study Example In ε = 0.012 decomposition, the network is decomposed into 82 Sub-Networks (SN), and therefore 82 LPS/NVD Agents are generated. A violation is simulated and is detected by NVD Agent 36 which is sent to LPS Agent 36. Currently we are implementing LPS agents to optimise nearest DG Agents outputs. For example, when ε = 0.012, the network is grouped into 82 subnetworks where 82 LPS/NVD Agents are activated. A violation is simulated and is detected by NVD Agent 36 (at nodes 1641, 1642, 1643, 1644, 1645, 1646, 1652) which is sent to LPS Agent. Currently we are implementing LPS agents to optimise nearest DG Agents outputs (i.e. DGs Agents 98, 99, 100, 146, and 177 by 0.30, 0.30, 0.30, 0.28, 0.30 p.u. respectively)

Agent Communications

In Progress Extend our proposed MAS for a real heavily meshed secondary network. Implement more functionalities of self-organizing, self-adaptive, and plug-and-play capabilities. A Case study for real heavily meshed network is under development We are implementing all agents using our proposed MAS for a real heavily mashed networks which contains 311 DGs. Subnetworks with all agents (ε Decomposition, LPS, NVD, and DG Agents) has been created and activated, and communication between agents has been established. Violation detection by NVD Agents has been implemented. We are developing more functionalities by ε Decomposition agent. We are also implementing LPS agents to optimise nearest DG Agents outputs and to trigger changing ε value as required.

Conclusion and Future Work Proposed a novel MAS framework for distributed intelligent control. Design and implement of MAS for distributed voltage control with ɛ decomposition. Four different agents are developed contain: ɛ decomposition agent; linear programming solver agent; network violation detection agent; distributed generator agent. Integration of MAS within the Presage2 platform for agent simulations and case studies. For the next step, the MAS framework will be extended for a model of a real heavily meshed secondary network. It will also add more control functionalities to achieve plug-and-play application for fully-integrated framework.