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Rodrigez Kabata, E. (2017). A Literature on Applications of Cooperative Control in Power Systems and Smart Micro-Grids. PHILICA.COM Article number 987.

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A Literature on Applications of Cooperative Control in Power Systems and Smart Micro-Grids

Emanuel Rodrigez Kabataunconfirmed user (University of the Andes, Merida)

Published in engi.philica.com

Abstract
In this paper, a literature review on the applications of cooperative control or distributed cooperative control in power systems is provided. A numerous number of papers and publications from several sources has been considered, and the most informative ones have been selected in this paper.

Article body

Abstract – In this paper, a literature review on the applications of cooperative control or distributed cooperative control in power systems is provided. A numerous number of papers and publications from several sources has been considered, and the most informative ones have been selected in this paper.

Centralized Vs. Decentralized Controller

Although centralized architectures have been used for primary control the standard is to employ decentralized proportional control loops locally at each inverter [1]-[7] in order to enhance redundancy and enable “plug-and-play” functionality [7].

Why Cooperative Control?

The first control technique is based on communication links such as the master-slave approach. Such techniques can be adapted in systems where DGs are connected to a common bus or located in close proximity. This is because it is impractical and costly to distribute the dynamic sharing signals, which are characterized by their high bandwidth in long connection distance. Furthermore, reliability issues of the centralized control approach might counteract the positive reliability boosts gained by implementing microgrids [8].

Applications in power systems

Balance between generation and load (Power Balance or Security), in control theory the primary control corresponds to a proportional controller (P controller). Limitations in primary control are: 1) The power plants participating in primary control have limited primary control reserves. The amount of capacity available for the primary control depends on the requirements for the system. 2) The gain must be fairly evenly distributed in the system, because otherwise the transmission grid might be overloaded. Primary control is fast, autonomous, and provides the first-line corrective action in disturbances [9]. The frequency of a large power system must be kept around the nominal value!restore the frequency to its nominal value, 2) Release used primary control reserves, 3) Reduce the integrated time error. Generally speaking, the goal of secondary control is to remove the aforementioned deviations in both global frequency and local voltage [9]. Secondary control operates by changing the base generation of the system (base generation is the generation at nominal frequency. Instead the frequency deviation leads in changing the power generation.

• Change generation in a power plant that is not participating in the primary control.

• Change base generation in a power plant that is participating in the primary control.

Secondary control takes place on a follow-up time scale and manages the deployment of resources to ensure reliable and economic operation [9].

Voltage Control

In grid-connect mode, the frequency and the voltage of the microgrid are maintained within a tight range by the main grid. In an islanded operation, however, which has relatively few microsources, the local frequency and the voltage control of the microgrid is not straightforward. During islanding, the power balance between supply and demand does not match at the moment. As a result, the frequency and the voltage of the micro grid will fluctuate, and the system can experience a blackout unless there is an adequate power-balance matching process.

The RES has an intermittent nature since their power outputs depend on the availability of the primary source, wind, sun, etc., and therefore, they cannot guarantee by themselves the power supply required by loads. On the other hand, CHP systems are limited by their insufficient dynamic performance for load tracking. Especially, the frequency of the micro grid may change rapidly due to the low inertia present in the micro grid. Therefore, local frequency control is one of the main issues in islanded operation. To overcome these limitations, the introduction of the ESS is considered as an effective solution. The ESS is based on power electronic device and has a very fast response time. Therefore, a properly designed ESS can allow a system to stabilize by absorbing and injecting instantaneous power. There are some previous studies on the application of the ESS for stabilizing the power systems and the RES. Voltage control is one of the most significant issues that limit the DG penetration in distribution systems. When DG units are interconnected to a distribution system, they can significantly change the system voltage profile and interfere with the conventional local control strategies of LTCs, line voltage regulators and shunt capacitors. This interference leads to overvoltage, under voltage, increasing in system losses and excessive wear and tear of voltage control devices.

[3] provides a proper voltage control 1) mitigate the interference between LTC and DG operation and guarantee a proper voltage regulation in all operating conditions, 2) relieve the stress on tap operation of LTC, and 3) avoid unnecessary DG active power curtailment, in case of multiple feeders having a substation LTC, unbalanced load diversity and high DG penetration for multiple feeders having a transformer tap-changer (LTC), unbalanced load diversity and multiple distributed generation (DG) units in each feeder. LTC and DG units are considered as control agents. Each control agent has a global and a self-objective. The interior structure of the control agents has been defined based on the BDI theory and the exchanging messages follow the FIPA communication acts. The proposed cooperative control scheme has the capability to properly regulate the voltage in all operating conditions.

Reference [7] proposes a distributed cooperative control of multi-agent systems, as a secondary voltage control of microgrids, the proposed secondary control is fully distributed; each distributed generator only requires its own information and the information of some neighbors. Input–output feedback linearization is used to convert the secondary voltage control to a linear second-order tracker synchronization problem. The control parameters can be tuned to obtain a desired response speed. The distributed structure obviates the requirements for a central controller and complex communication network which, in turn, improves the system reliability.

In [8], a method for constructing a deregulated multi-agent power system in which Voltage and Reactive Power are controlled by employing multiple agents to decrease the total number of actions needed to control network conditions. In a deregulated multi-agent power system, each agent monitors and controls the system within pursuant to its constraints. They are capable of communicating with outside and also changing their internal state based on the received data. Agents are installed in sub-stations and agents from different voltages levels can cooperate with each other. The simulation results show that the system provides the same control performance with fewer actions than that of conventional systems.

This paper [9] conceives self-organizing distributed (non-hierarchal) voltage control architecture based on cooperative fuzzy agents to improve the profile of bus voltage magnitude. The distributed agents use traditional sensors to acquire local bus variables. Unanimous protocols are used to assess the main variables, state variables, to find out the Smart Grid situation. The process of the variables are done by a fuzzy-based solution algorithm like other self-organizing biological populations, the voltage control is achieved by the local interaction of fuzzy agents as that of the theory distributed consensus. According to the simulation outcomes, Fuzzy Logic and Fuzzy Agent-based architecture allows agents to control reactive power injection with a good speed. Overall results show this architecture is a promising approach for voltage control problem in smart grids.

In [10], during islanding, frequency and voltage can be regulated to normal values if power-balance is met. The response time of diesel generators, gas engines, and fuel cells are relatively slow, but the response time of inverter-based ESS are about milliseconds. ESS’s have a limited capability to provide the demand power because of their limited capacity. Therefore, the power output of an ESS should be brought back to zero as soon as possible in order to secure the maximum controlling reserve.  cooperative control strategy of Microsources and Energy Storage Systems (ESS) during islanded is addressed and assessed by a simulation and experiment for controlling both frequency and voltage. Methodology: two-layer cooperative control structure is introduced, including 1) primary control action in the ESS, which follows The constant frequency and constant voltage (CFCV) control, 2) secondary control action in the microgrid management system (MMS) that regulates the power output of the ESS to be zero. frequency and voltage are regulated well. Control capability of the system is improved by the secondary control action.

In [11], a decentralized control method for Electronically Interfaced Distributed Generations (EI-DGs) that controls their output frequencies, power generations, and voltages during grid-connected, islanding, and synchronizing modes. The proposed control system utilizes droop control to quickly balance generation and demand after sudden disturbances. It utilizes distributed cooperative control to stabilize the system frequency and voltage, and also to distribute the generation among DGs.  Furthermore, the proposed control system adjusts the frequency and voltage to reconnect the microgrid to the main grid by utilizing only local measurements from the neighboring DGs.

Reference [12] proposes the concept of a decentralized non-hierarchal voltage regulation architecture based on intelligent and cooperative smart entities. (1) This paper also proposes a distributed and cooperative optimization strategy (1) to calculate the actual value of the cost function and its gradient, marginal price. (2) To find the optimum asset of the voltage controllers. Each controller is composed of a set of sensors acquiring local bus variables (i.e., voltage magnitude and active and reactive bus power) and of a dynamical system (oscillator) initialized by sensor computations. The oscillators of nearby controllers are mutually coupled by proper local coupling strategies derived from the mathematics of populations of mutually coupled oscillators. This biologically inspired paradigm allows all the oscillators to quickly synchronize to the weighted average of the variables sensed by all the controllers in the Smart Grid. Owing to this feature, each voltage controller can assess, in a totally decentralized way, the many important variables characterizing the operation of the global Smart Grid (i.e., mean grid voltage magnitude and power losses). This method employs traditional sensors to acquire local bus variables (i.e., voltage magnitude and active and reactive bus power) and mutually coupled oscillators to assess the main variables that characterize the global operation of the Smart Grid. These variables are then processed by distributed optimizers in order to identify proper control actions aimed at minimizing the objective function describing the voltage regulation objectives. The results obtained on a test power grid show that this regulation paradigm allows smart controllers to detect local voltage anomalies since they know both the performances of the monitored buses, computed from locally acquired information, and the global performance of the entire grid, computed by local exchanges of information with neighboring nodes. The convergence of this process corresponds well with the time constraints characterizing the voltage regulation process in Smart Grids. This is achieved without the need for a central fusion center acquiring and processing all the node acquisitions. This makes the overall monitoring architecture highly scalable, self-organizing, and distributed.

The electric power grid has traditionally been made up of a large number of protection and control devices that act on local information to respond to problems. This method works well in some cases, but is inefficient in many others. Agents have begun to be recognized as a natural solution to this problem in the electric power research community. Their autonomous nature, ability to share information and coordinate actions, and the potential to be easily replaced from remote facilities make them potentially valuable [13].

Intelligent adaptive methods allowing protective devices to adapt to changing system conditions.  Each agent may have only a local view of the system, but the team of agents (i.e. multi-agent system) can perform wide-area protection schemes through autonomous and cooperative action of the agents. The cooperation of multi-agent enables the flexible utilizations of all hardware resources by any equipment, so that various adaptive protection functions are realized. Measurement Agent: CT, VT, CB; Protector Agent: collects information, detects a fault, and transmits trip signal to performer agent; Performer Agent: receives trip signal from local and neighboring station protector agent, then trip CBs; Mobile Agent: moves between software agents or protection system to distribute data; Region Agent; gathers data in lower layers and sends them to management agents and highest layer; calculates the setting of the relays according the changes of power system and functions proper to each pieces of equipment and pass back to protector agents; System Agent: produce the essential data to recognize a disturbance and transfer them to evaluation agent. Evaluation Agent: evaluates the performance of protection system based on the data from System Agent. If any violation or lack of protection capability is found, evaluation agent sends a request to modify setting values to management agent [14].

The authors of [15] first introduced the new concept of “relay agent” to realize cooperative protection system to provide a powerful means of adaptive protection functions. By combination of microprocessor and communication technologies. Relay agents are classified by their roles. Simulations indicate that the concept enables the protection system to keep an isolated zone minimum against any changes in power system conditions and to secure high reliability of the protection system with less redundant hardware.

In [16], a cooperative protection system with a layered distributed structure based on multi-agent system to improve the performance of protection system, they focused on stepped-distance protection. They defined 8 agents, communicating with each other in different layers, from measurement to management agents. A fault can be detected locally by the lowest agents, at the same time, the system and management agents owing to have a wide monitoring can find the un-detected or wrongly detected faults, consequently, they can send corrective orders to the lower agents. A more reliable fault detection system with a lower backup operation time than those of conventional protection is obtained. Protection functions adapts with changes in power

In [17], a wide research results upon the potentials of implementing Multi-Agent Systems (MAS) technology on Tehran-Iran power distribution network to gain high degrees of independency, is proposed. It is shown that, MAS technology’s impact on outage management, upon medium-voltage (MV) power distribution lines, makes it be autonomously performing and effective cooperative

Intro Non-linear time variant loads are increasing weather in household or in industry [18], at same time, the penetration of DGs is also growing [19]. Both mentioned things increase the distortion and disturbances in the voltage signals.  Therefore, power quality issues must be considered in Smart Grids.

In [20], the authors practically made a voltage monitoring device to provide an economical solution for monitoring the power quality. Their system is based on a compact microprocessor module. Nowadays, microprocessors have enough computation power to calculate monitoring system related functions. They also include several peripheral devices in very low cost such ADC, Ethemet chip, built-in TCP-IP protocol, I/O ports, RAM/flash memory. According to the network connecting abilities of microprocessors the data can be presented via communication networks. Their innovation is useful for both investigation and monitoring. Such devices are easy to be installed because of their smallness and low-cost.

In [21], a decentralized control algorithm for charging management of Plug-in Hybrid Electric Vehicles (PHEVs) in distribution networks. The objectives of the proposed control algorithm are to mitigate PHEV integration challenges (e.g., over-currents and under-voltages in distribution networks) and to reduce the charging costs of PHEVs. The proposed algorithm adjusts the charging rates of PHEV chargers utilizing distributed cooperative control to prevent the network constraints (i.e., voltage and current limits) from being violated. It also determines the operating modes of the chargers (i.e., charging, discharging, or idle) using a decision making algorithm to increase the State Of Charges (SOCs) and decrease the charging costs only based on the current conditions of the distribution network.

In [22] the employment of self-organizing sensor networks is studied to amend fully decentralized voltage quality monitoring architecture. The idea is to start from the mathematics of populations of mutually coupled oscillators for designing high pervasive/self-organizing sensor networks for voltage quality monitoring in electricity distribution systems specifically, we propose the employment of a cluster of sensor networks each one monitoring a specific electrical grid section. Each network node is com-posed by a sensor, that acquires the voltage wave forms and computes the corresponding quality index (i.e. node index), and of a dynamical system (oscillator) initialized by the sensor computation. We show that if the oscillators of nearby nodes of the same sensors network are mutually coupled by proper local coupling strategies, then each dynamic system on each node converges to the global voltage quality index of the monitored grid section. By inquiring any node of the corresponding sensors network without the need of a central fusion center acquiring and processing all the node acquisitions [23]. According to this paradigm each node can assess both the performances of the monitored site, computed by acquiring local information, and the global performances of the monitored grid section, computed by local exchanges of information with its neighbor’s nodes. This feature allow system operators to assess the system voltage quality index for each grid section by inquiring any node of the corresponding sensors network without the need of a central fusion center acquiring and processing all the node acquisitions. This makes the overall monitoring architecture highly scalable, self-organizing and distributed [24].

 

References

[1]  J. A. P. Lopes, C. L. Moreira, and A. G. Madureira, “Defining control strategies for microgrids islanded operation,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 916–924, 2006.

[2] S. Barbarossa, “Self-organizing sensor networks with information propagation based on mutual coupling of dynamic systems,” in Proc. Int.Workshop Wireless Ad hoc Netw., New York, May 23–26, 2005.

[3] M. di Bisceglie, C. Galdi, A. Vaccaro, and D. Villacci, “Cooperative sensor networks for voltage quality monitoring in Smart Grids,” in Proc. IEEE Powertech Conf., Bucharest, Romania, Jun. 28–Jul. 2, 2009,pp. 1–6.

[4]  A. Tuladhar, H. Jin, T. Unger, and K. Mauch, “Parallel operation of single phase inverter modules with no control interconnections,” in Applied Power Electronics Conference and Exposition, Atlanta, GA, USA, Feb. 1997, pp. 94–100.

[5]  S. Barsali, M. Ceraolo, P. Pelacchi, and D. Poli, “Control techniques of dispersed generators to improve the continuity of electricity supply,” in IEEE Power Engineering Society Winter Meeting, New York, NY, USA, Jan. 2002, pp. 789–794.

[6]  R. Majumder, A. Ghosh, G. Ledwich, and F. Zare, “Power system stability and load sharing in distributed generation,” in Power System Technology and IEEE Power India Conference, New Delhi, India, Oct. 2008, pp. 1–6.

[7]  Y. U. Li, C.-N. Kao, “An accurate power control strategy for powerelectronics-interfaced distributed generation units operating in a low voltage multi-bus microgrid,” IEEE Transactions on Power Electronics, vol. 24, no. 12, pp. 2977–2988, 2009.

[8]  J. M. Guerrero, J. C. Vasquez, J. Matas, M. Castilla, and L. G. de Vicuna, “Control strategy for flexible microgrid based on parallel line-interactive UPS systems,” IEEE Transactions on Industrial Electronics, vol. 56, no. 3, pp. 726–736, 2009.

[9] John Undrill, “Power and Frequency Control as it Relates to Wind-Powered Generation,” December 2010. 

[10] G. Diaz, C. Gonzalez-Moran, J. Gomez-Aleixandre, and A. Diez, “Scheduling of droop coefficients for frequency and voltage regulation in isolated microgrids,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 489–496, Feb. 2010.

[11] R. Jalilzadeh Hamidi, H. Livani, S. H. Hosseinian, and G. B. Gharehpetian, “Distributed cooperative control system for smart microgrids,” Electric Power System Research, Vol. 130, pp. 241-250, 2016.  

[12] C. K. Sao and W. Lehn, “Control and power management of converter fed microgrids,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1088–1098, Aug. 2008.

[13] P. H. Divshali, A. Alimardani, S. H. Hosseinian, and M. Abedi, “Decentralized cooperative control strategy of microsources for stabilizing autonomous VSC-based microgrids,” IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1949–1959, Nov. 2012.

[14] J. M. Guerrero, J. C. Vasquez, J. Matas, L. G. de Vicuna, and M. Castilla, “Hierarchical control of droop-controlled AC and DC microgrids–a general approach toward standardization,” IEEE Transactions on Industrial Electronics, vol. 58, no. 1, pp. 158–172, 2011.

[15] John W. Simpson-Porco, Qobad Shafiee, Josep M. Guerrero, “Stability, Power Sharing, & Distributed Secondary Control in Droop-Controlled Microgrids,”

[16] Nagata, T.; Nakachi, Y.; Hatano, R., “A Multi-agent Cooperative Voltage Control Method,” Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE Digital Object Identifier: 10.1109/PES.2008.4596598 Publication Year: 2008, Page(s): 1 - 6

[17] Verification of Cooperative Control Method for Voltage Control Equipment on Distribution Network Simulator Considering Interconnection of Wind Power Generators Kubota, Y. ; Genji, T. ; Miyazato, K. ; Hayashi, N. ; Tokuda, H. ; Fukuyama, Y. Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES Volume: 2 Digital Object Identifier: 10.1109/TDC.2002.1177640 Publication Year: 2002, Page(s): 1151 - 1156 vol.2 Cited by:  Papers (4)

[18] A Novel Cooperative Protocol for Distributed Voltage Control in Active Distribution Systems, Farag, H.E.Z., El-Saadany, E.F. Power Systems, IEEE Transactions on Volume: 28, Issue: 2 Digital Object Identifier: 10.1109/TPWRS.2012.2221146 Publication Year: 2013, Page(s): 1645 – 1656.

[19] Qobad Shafiee, Student Member, IEEE, Josep M. Guerrero, Senior Member, IEEE, and Juan C. Vasquez, Member, IEEE, “Distributed Secondary Control for Islanded Microgrids—A Novel Approach,” IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 29, NO. 2, FEBRUARY 2014.

[20] Loia, V.; Vaccaro, A.; Vaisakh, K., “A Self-Organizing Architecture Based on Cooperative Fuzzy Agents for Smart Grid Voltage Control,” Industrial Informatics, IEEE Transactions on Volume: 9 , Issue: 3, Digital Object Identifier: 10.1109/TII.2013.2249074, Publication Year: 2013 , Page(s): 1415 – 1422.

[21] R. J. Hamidi, H. Livani, “Myopic real-time decentralized charging management of plug-in hybrid electric vehicles,” Electric Power System Research, Vol. 143, pp. 522-543, 2017. 

[22] Jong-Yul Kim, Jin-Hong Jeon, Seul-Ki Kim, Changhee Cho, June Ho Park, Hak-Man Kim, and Kee-Young Nam, “Cooperative Control Strategy of Energy Storage System and Microsources for Stabilizing the Microgrid during Islanded Operation,” IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 25, NO. 12, DECEMBER 2010.

[23] A. Amorim, A. L. Cardoso, J. Oyarzabal, and N. Melo, “Analysis of the connection of a microturbine to a low voltage grid,” presented at the Int. Conf. Future Power Syst., 16–18 Nov., 2005, Amsterdam, Netherlands.

[24] A. K. Saha, S. Chowdhury, S. P. Chowdhury, and P. A. Crossley, “Modeling and performance analysis of a microturbine as a distributed energy resource,” IEEE Trans. Energy Convers., vol. 24, no. 2, pp. 529–538, Jun. 2009.

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Rodrigez Kabata, E. (2017). A Literature on Applications of Cooperative Control in Power Systems and Smart Micro-Grids. PHILICA.COM Article number 987.


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