Cooperative Load-Shedding Control of Agent-based Islanded Microgrid 1

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1 Cooperative Load-Shedding Control of Agent-based Islanded Microgrid 1 1 Yujin Lim, 2 Hak-Man Kim, 3 Tetsuo Kinoshita 1, First Author Department of Information Media, University of Suwon, yujin@suwon.ac.kr *2,Corresponding Author Department of Electrical Engineering, University of Incheon, hmkim@incheon.ac.kr 3 Graduate School of Information Science, Tohoku University, kino@riec.tohoku.ac.jp Abstract A Key component to future smart grids may be a microgrid system capable of integrating generation, load, and storage assets into an autonomous power system entity. A microgrid is intrinsically distributive in nature. In order to improve performance of a microgrid, a distributed and cooperative control is required. Agent technology is one of techniques for achieving the objectives of distributed control of a microgrid. In this paper, we present agent-based load-shedding system in an islanded microgrid. In order to maximize load management and fairness, we define a control architecture, functionalities of agents, interactions among agents, and decision-making strategies for load shedding. The designed system is intended to lay the groundwork the cooperative and integrated control of load shedding in a microgrid. Keywords: Microgrid, Islanded Microgrid, Load Shedding, Multi-Agent System, Smart Grid 1. Introduction Over the years, the growing demand of electric power and the increase in the price of fossil fuels along with the CO 2 emission, renewable energy sources have been seen as an interesting solution. To make use of the renewable energy sources efficiently, a microgrid concept provides an effective approach. A microgrid is an emerging frontier in meeting the challenges in providing reliable power supply in a small community [1]. A microgrid is described as a set of distributed generations (DGs), distributed storages (DSs), and loads. The architecture of a microgrid is an open avenue of research which deals with the operation of the components in order to meet common objectives under technical, social, and economic constraints. There is innovation taking place in the technical standards that enable a microgrid. IEEE 1547 focuses maintenance, operation, performance, safety considerations, and testing requirements with regard to interconnection of a distributed generation resource with the power grid. Within IEEE 1547 family of extensions, IEEE is a standard that provides additional guidance on a microgrid [2]. It covers key consideration for planning and operating a microgrid: impacts of voltage/frequency/power quality, protection schemes and modifications, monitoring, information exchange and control, understanding load requirements of the customer, identifying steady state and transient conditions, understanding interactions between machines, load shedding, demand response, and cold load pickup. A microgrid is intrinsically distributive in nature. In order to improve performance of a microgrid, a distributed and cooperative control is required. Agent technology is one of techniques for achieving the objectives of distributed control of a microgrid [3]. An agent is a software or hardware entity that exhibits characteristics of autonomy, self-organization, and decentralization so as to progress the entire system toward a common goal such as in cooperative distributed problem solving [4, 5]. In this paper, agent-based control system for a microgrid is designed for a load shedding to balance between the load and power generation in an islanded operation mode. First of all, the control architecture for a load shedding is defined and the functionalities of components in the architecture are defined. Then, information flow among components and decision-making strategies are presented. The remaining paper is organized as follows: Section 2 introduces an islanded operation mode in a microgrid. Section 3 describes a load-shedding problem. In Section 4, we propose an agent-based load- 1 This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology( ). Advances in information Sciences and Service Sciences(AISS) Volume4, Number18, Oct 2012 doi: /AISS.vol4.issue

2 shedding system which includes agent control algorithms, interactions among the agents, and description of decision making for the load shedding. Finally, conclusion and future work are drawn in Section Islanded operation A microgrid operates in either a grid-connected mode or an islanded mode. In a gridconnected mode, a microgrid can exchange energy with the power grid. In other words, the difference between the power generated and the power demanded is balanced by trading with the power grid. An islanded mode means an isolated operation from the power grid intentionally for maintenance purposes or economic reasons, or unintentionally for physical damage to power grid components. In the island mode, various techniques, such as load shedding, are used to balance between the load and generation. If load requirements are larger than the generation, it is necessary to disconnect some load or reduce power consumption by other means. The microgrid decides the amount of reactive load acceptance and rejection to maintain system voltage stability and to prevent overvoltage excursions beyond the limits of the system. Besides, it is necessary to provide sufficient monitoring in order to operate and understand the status of the microgrid. The microgrid has two distinct architectures based on centralized and decentralized philosophies of supervisory control. The centralized architecture involves the use of local controllers that are coordinated by the microgrid central controller (we call it microgrid operation and control center, MGOCC [3]). In other words, local controllers adjust generation or demand levels based on MGOCC-issued signals. In the decentralized architecture, local controllers have the maximum autonomy. The autonomy implies that local controllers are intelligent and can communicate with each other to form a larger intelligent entity. Generally, they are organized in multi-level hierarchical architecture. In other words, several controllers at the field level interact with controllers at the medium level. The controllers at the medium level perform management operations and interact with higher level controller. When the microgrid is in a single ownership or under a single control policy, the centralized architecture is suitable. When the microgrid is in multiple ownerships or under multiple control policies, the decentralized architecture is suitable. In this paper, we focus on the centralized architecture under a single control policy because centrally-oriented decision-making system simplifies implementation and system complexity. The microgrid operates in two steps - planning and implementation - like conventional power systems. In other words, an operational plan for the interval i+1 is made during the interval i and the plan is carried out during the interval i+1. The MGOCC is associated with management of the microgrid operation. It gathers information from the microgrid system and monitors status of the system. Based on the gathered information, it makes the operational plan for the next interval and informs the system of the plan. Finally, it monitors the implementation of the plan. 3. Load shedding Load shedding (LS) is a corrective action that forces the perturbed system to a new viable operational state [6]. It is defined as a coordinated set of controls which results in decrease of the electric load in the microgrid. An optimal load-shedding scheme finds the best stable equilibrium operating point for the system with the minimum amount of load to be shed. In conventional under frequency load-shedding scheme, whenever frequency of the system falls below predetermined thresholds, parts of the system load are shed in some predetermined steps [7]. In the first step, the anticipated overload (L) is determined as below. =. (1) 272

3 All of the required loads to be disconnected can be shed simultaneously, however this approach may lead to overshedding for small disturbances. A suitable alternative is to divide the loads to be dropped among several steps and shed a portion in each step. Thus, the second step is to select the number of load shedding steps. The proper number of steps has been verified by dynamic studies [8]. Typically 3 to 6 steps are recommended. In the third step, the total amount of load to be shed is calculated to maintain frequency above minimum permissible frequency for the maximum anticipated overload, as below [9]. = ( ) ( ), (2) where LD is the total load to be shed, f is the minimum permissible frequency, d is the load reduction factor, and is the nominal system frequency. Afterward, the total load needs to be divided among the load shedding steps. Besides, in each step, the amount of load to be shed for each load needs to be determined. Division of load to be shed among load shedding steps and division of load to be shed among loads in a step have been implemented in various ways. Several algorithms have been proposed in the literature. Many of these algorithms utilize the rate of change of frequency (df/dt) to recognize the amount of load to be shed [10, 11]. In [12] for large disturbances which result in a high rate of frequency decline, the amount of load to be shed is increased. Meanwhile in [13], the frequency settings of the underfrequency relays are increased. Recently, in some literature [14-16], they solve the load-shedding problem to determine the load to be shed by using a game theoretic approach. They consider the power system as a collection of individual components that compete for system resources. The game theoretic approach is to maximize the system performance and to minimize the total customer interruption cost. In this paper, we design the centrally-oriented decision-making system for load shedding. It needs fast and reliable communication of the measured parameters of components in the microgrid. The communication can be through telephone lines, power line carriers, or wireless medium. For simplicity, we assume that the communication is instant, noiseless, and secure i.e., the measurement can be conveyed without any delay, without any packet loss, and in a way not susceptible to eavesdropping or interception [17]. 4. Agent-based Load Shedding for Islanded Microgrid A multi-agent system (MAS) is a collection of autonomous computational entities (agents) that possess the ability to perceive aspects of their environment. The agents have varying degrees of intelligence based on their roles within the architecture. The agents pursue goals as to optimize certain performance measures within an environment which can be hard to define analytically Control architecture For the purpose of agent design for load shedding in the microgrid, we divide microgrid components into four classifications. Diverse microgrid components are grouped according to operational capabilities and assigned appropriate control agents. The component classes are DG (or DG agent), Load (or Load agent), DS (or DS agent), and MGOCC-LS (or MGOCC-LS agent). We extend MGOCC [3] to include load-shedding functionalities and we call it MGOCC-LS. The following describes agent types and responsibilities associated with load shedding (The extended functionalities for the load shedding are specified by the sentence when the load shedding is initiated ). DG agent - Determine the amount of available power supplied and the cost per unit of the power by attached component for the next operational interval - Inform MGOCC-LS agent of the available amount of power supplied with the cost per unit - Informed of the final amount of supply power granted by MGOCC-LS agent 273

4 - Give a command to attached component regarding startup, shutdown, or variation of configuration based on the quantity of power granted by MGOCC-LS agent - Monitor the amount of power supplied by attached component Load agent - Anticipate and determine the amount of power demanded by attached component for the next operational interval - Inform MGOCC-LS agent of the amount of load demanded - (When the load shedding is initiated) Determine the bidding price and amount according to its bidding strategy - Informed of the final amount of load granted by MGOCC-LS agent - Give a command to attached component regarding startup, shutdown, or variation of configuration based on granted consumption levels - Monitor the amount of power consumed by attached component DS agent - Informed of the power balance by MGOCC-LS agent for the next operational interval - Determine the amount of state of charge (SOC) of attached component - Determine the its role as a power supplier or a power consumer - Inform MGOCC-LS agent of the available amount of power supplied with the cost per unit (as a power supplier) or the amount of load demanded (as a power consumer) - Give a command to attached component regarding charge, discharge, or quantity of power to be charged/discharged - Monitor the amount of power to be charged/discharged by attached component MGOCC-LS agent - Initiate operational planning of the microgrid every operational interval - Communicate with other agents to gather the information for the operational plan of the next interval - Check power balance by using the gathered information - Initiate load shedding when total power supply is less than total power demand - (When the load shedding is initiated) Make the first decision on the amount of load to be shed - (When the load shedding is initiated) Check the remaining power after the first decision-making - (When the load shedding is initiated) Communicate with Load agents to gather their bidding information for the remaining power when there is any remaining power - (When the load shedding is initiated) Make the second decision on the allocation of the remaining power - Inform other agents of the final amount of supply power or the final amount of load - Monitor operational implementation of the microgrid 4.2. Description of load-shedding decision-making The primary purpose of the load shedding in the microgrid is twofold: maximize load management and maximize fairness. In this paper, the load management indicates to increase the power utilization at loads in practice. The fairness indicates whether loads are received a fair share of the supplied power. The conventional load-shedding schemes have considered that the load uses a continuous range of values to present its load demand. However, in reality, some loads use integer and discrete values. We design the load-shedding system with consideration of the discrete characteristic of load demands in order to maximize the load management and the fairness. Our load-shedding system operates in two steps. In the first step, the amount of load to be shed is decided for each load. To do this, we can use a game theoretic approach, i.e., Nash Bargaining Solution (NBS) [16]. Even though the amount of load to be shed is determined by the optimization solution such as a game theory, it is possible to have any remaining power due to the discrete characteristic of load demands. The remaining power (R) is calculated as below. = ( h ) (3) 274

5 Thus, we devise the second step of our load-shedding system to fairly allocate the remaining power to loads (we also call it the bidding step). The remaining power allocation is determined according to the bidding price of a load intending to buy the remaining power. In the second step, we can assume that the bidding prices of loads are the same or loads have their own bidding prices to maximize profits [16]. For example, total power supplied is 100kWh, three loads demand the power, i.e., L 1 =30kWh, L 2 =50kWh, and L 3 =60kWh, and each load demand is composed of sub load demands, i.e., L 1 = , L 2 = , and L 3 = In the first step of our load-shedding system, the load to be shed is determined, i.e., 20 for L 1, 19 for L 2, and 20 for L 3. Thus, the remaining power (R) is 100 (140 59) = 19kWh. In the second step, we assume that L 1 and L 2 intend to buy the remaining power. In other words, L 1 bids 20(=13+7) with its bidding price 5 cents/kwh and L 2 bids 19(=6+13) with its bidding price 4 cents/kwh. Through the decision-making in the second step, the power 13 and 6 are allocated to L 1 and L 2 according to their bidding price, respectively. Finally, the amount of load to be shed is determined, i.e., 7 for L 1, 13 for L 2, and 20 for L Message flow One of key functionalities of MAS is the communication among the distributed agents. By utilizing the Foundation for Intelligent Physical Agents (FIPA) guidelines for agent-based communication, the interoperability and communication protocols for MAS are standardized. In the literature [3], FIPA- Contract Net Interaction Protocol (CNIP) performatives are extended for agent communication in the microgrid. It has split a PROPOSE message into two different messages, i.e. PROPOSE SUPPY for power suppliers and PROPOSE LOAD for power consumers. It is because the information sent from power suppliers and the information sent from power consumers are different. In other words, agents of power suppliers send their supply amounts and prices and agents of power consumers send their load demands, when the agents send their PROPOSE messages. In this paper, based on [3], we extend FIPA-CNIP performatives for load shedding in the microgrid as Table 1. In Table 2, the purpose of the extended performatives for load shedding is described FIPA-CNIP CFP PROPOSE Table 1. Comparison with FIPA-CNIP Extended FIPA-CNIP Extended FIPA-CNIP for microgrid [3] for load shedding CFP, CFP CFP LS BIDDING PROPOSE SUPPLY, PROPOSE SUPPLY, PROPOSE LOAD, PROPOSE LOAD PROPOSE LS BIDDING REFUSE ACCEPT REJECT FAILURE INFORM 275

6 Table 2. Extended FIPA-CNIP performatives for load shedding Performative Purpose CFP sent by MGOCC-LS agent to initiate the microgrid operation process CFP LS BIDDING sent by MGOCC-LS agent to Load agent in order to initiate the bidding step of load shedding PROPOSE SUPPLY sent by DG agent to MGOCC-LS agent in order to propose the amount of power supplied with a price PROPOSE LOAD sent by Load agent to MGOCC-LS agent in order to propose the amount of load demanded PROPOSE LS BIDDING sent by Load agent to MGOCC-LS agent in order to propose its bidding price and amount to buy the remaining power REFUSE sent by DG, Load, or DS agent to refuse to send a proposal ACCEPT sent by MGOCC-LS agent to accept a proposal REJECT sent by MGOCC-LS agent to reject a proposal FAILURE sent by DG, Load, or DS agent to tell a requested action failed INFORM sent by DG, Load, or DS agent to tell results of action carried out Figure 1. Message flow among agents for load shedding in the microgrid 276

7 Figure 2. The algorithm of MGOCC-LS agent for load shedding Figure 3. The algorithm of Load agent for load shedding Figure 1 shows the message flows based our extended FIPA-CNIP performatives. In the figure, the upper box is for the microgrid operation defined by [3], and the lower box is for the load shedding proposed by this paper. First of all, MGOCC-LS sends CFP message to DG and Load agents to make an operational plan for the next interval. DG and Load agents respond with PROPOSE SUPPLY and PROPOSE LOAD messages, respectively. Once MGOCC-LS receives the messages from DG and Load agents, it checks the balance between power supply and power demand. Then, MGOCC-LS informs DS agent of the information of power balance. DS agent decides its role as a power supplier or a power consumer and sends PROPOSE SUPPLY (as a power supplier) or PROPOSE LOAD (as a power consumer). When MGOCC-LS agent receives the messages from DS agents, it finally checks the power balance. If the total power supply is less than the total power demand, MGOCC-LS initiates the load shedding. In the first step of the load shedding, MGOCC-LS determines the load to be shed. Then, if there is any remaining power, MGOCC-LS starts the second step of the load shedding by sending CFP LS BIDDING messages to Load agents. When Load agents receive the messages and they intend to buy the remaining power, they respond with PROPOSE LS BIDDING messages including their bidding prices and amounts. Once MGOCC-LS gathers the bidding information from Load agents concerned, it allocates the remaining power to the Load agents and finally decides the final amount of load to be shed. Lastly, MGOCC-LS informs agents of the final amount of supply power or the final amount of load granted by it. Figure 2 and Figure 3 show the detailed algorithms of MGOCC-LS agent and Load agent for load shedding in the microgrid. 5. Implementation of the Load-Shedding System In this paper, we implement the proposed system by using the Agent-based Architecture of Distributed Information Processing System (ADIPS)/Distributed Agent System based on Hybrid Architecture (DASH) framework as an agent platform [18]. In order to show the functionality 277

8 and feasibility of the proposed system, we configure our system with one MGOCC-LS, three DGs, and ten Loads. The total supply is 900kWh = {300, 200, 400}. The total load demand is 1300kWh = {30, 50, 60, 100, 110, 120, 150, 200, 230, 250}. A load demand of a load agent is composed of a set of sub load demands, e.g., the load demand of Load 10 = 250kWh = {15, 34, 43, 13, 15, 11, 27, 6, 32, 28, 26}. We set that the number of sub load demands for each load is less than 12 (we call it the max. number of sub load demands). Our system is implemented in a distributed environment based on 6 PCs. For load-shedding decision-making, we use NBS in the first step and, in the second step, we assume that the bidding prices of loads are the same [16]. Figure 4 shows three windows of MGOCC-LS agent and a window of Load agent. Figure 4 (a) shows that MGOCC-LS gathers information of power supply and load demand from DG and Load agents for operational planning of the next interval. It checks power balance by using the gathered information. According to our configuration, i.e., total power supply (800kWh) is less than total power demand (1300kWh), MGOCC-LS initiates the load shedding. Operations of the first step in the load shedding are shown in Figure 4 (b). In the first step, the amount of load to be shed is determined, i.e., 500kWh = {0, 0, 0, 11, 42, 28, 46, 106, 122, 145}. After the first step, there is 100kWh remaining power. We assume that, among 10 Loads, 5 Loads agents intend to buy the remaining power with the same bidding price. The total bidding quantities is 250kWh = {30, 40, 50, 60, 70}. Figure 4 (c) shows the operations of the bidding step as the second step. In the bidding step, the remaining power is allocated to 5 loads according to the pattern of their sub load demands, i.e., {10, 30, 10, 30, 20}. Figure 4 (d) shows that Load agent exchanges message with MGOCC-LS agent. Figure 5 shows the running time of the first step of our load shedding. The dominant factor of the running time of our load-shedding system is the running time of the first step operation. It is because the determining an NBS is an NP-hard problem [19]. However, the solution of NBS involves maximizing a concave function over a convex set and there are some literature to solve the game in polynomial time [20]. In the figure, we change the number of load agents from 2 to 20 and the max. number of sub load demand for each load from 5 to 15, in order to show the feasibility of our system in various scenarios. We can see that the running time of our system is less than 1 minute in the various scenarios. (a) (b) (c) (d) Figure 4. Windows of agents in our system 278

9 Figure 5. The running time of our load-shedding system. 6. Conclusion We have presented an agent-based cooperative control system for load shedding in the islanded microgrid. The system includes control algorithms of agents composing the microgrid, interaction among the agents, extension of performatives, and description of decision making for the load shedding. The proposed system has provided a flexible and scalable architecture that allows the agents to solve the load-shedding problem in a cooperative way. The proposed system serves to bring out the numerous avenues for research in load shedding field in the microgrid. Many challenges remain to be overcome in terms of reliability, bidding strategy, interoperability, and security. We plan to do research on optimal bidding strategy to maximize the profit of loads in a long run. 6. References [1] N. D. Hatziargyriou, Microgrids, IEEE Power Energy, vol. 6, no. 3, pp , [2] IEEE Standard , Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems, IEEE, [3] H.-M. Kim, T. Kinoshita, A Multiagent System for Microgrid Operation in the Gridinterconnected Mode, KIEE Journal of Electrical Engineering & Technology, vol. 5, no. 2, pp , [4] S. K.C. Lo, "A Cooperative Multi-Agent Environment for Mobile Peer-to-Peer Security System", AICIT Journal of Convergence Information Technology, vol. 6, no. 9, pp , [5] L. Meng, M. Lu, J. Lai, X. Xu, "A Multi-agent Based Approach to Reliability Prediction of Train s Control and Monitoring Software System", AICIT International Journal of Digital Content Technology and its Applications, vol. 6, no. 12, pp , [6] M. A. Mostafa, M. E. El-Hawary, G. A. N. Mbamah, M. M. Mansour, K. M. El-Nagar, A. M. El- Arabaty, A Computational Comparison of Steady State Load Shedding Approaches in Electric Power Systems, IEEE Transactions on Power Systems, vol. 12, no. 1, pp , [7] A. Saffarian, M. Sanaye-Pasand, H. Asadi, Performance Investigation of New Combinational Load Shedding Schemes, In Proceedings of the IEEE International Conference on Power System Technology and Power India Conference (POWERCON), pp. 1-8, [8] P. M. Anderson, M. Mirheydar, An Adaptive method for Setting Underfrequency Load Shedding Relays, IEEE Transactions on Power Systems, vol. 7, no. 2, pp , [9] H. Seyedi, M. Sanaye-Pasand, M. R. Dadashzadeh, Design and Simulation of an Adaptive Load Shedding Algorithm using a Real Network, In Proceedings of the IEEE Power India Conference, pp. 1-5, [10] B. Delfino, S. Massucco, A. Morini, P. Scalera, F. Silvestro, Implementation and Comparison of Different Underfrequency Load Shedding Schemes, In Proceedings of the IEEE Power Engineering Society Summer Meeting, pp ,

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