Agent-Based Power Routing in Active Distribution Networks

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Agent-Based Power Routing in Active Distribution Networks Phuong H. Nguyen, Member, IEEE, Wil L. Kling, Member, IEEE, and Paulo F. Ribeiro, Fellow, IEEE

Abstract—The expected large-scale implementation of distributed generation (DG) requires a change in the current structure and operation of distribution networks. The future distribution network must be able to manage power flow in a bidirectional way, cope with uncertainties of renewable power generation and adjust to demands of more sophisticated customers. This paper introduces the concept of a power routing function to avoid congestion, minimize the operating cost, and adequately serve the requirements of customers. This function considers the optimal power flow as a problem of minimum cost flow in the graph theory. The Scaling Push-Relabel (SPR) algorithm is used to solve that problem. It will be implemented in a distributed agent environment which is suitable with a design concept of Active Distribution Networks. The performance of the power routing function is tested on a simulation of the medium voltage 32-bus network. Simulation results show the effectiveness and flexibility of the proposed function in dealing with issues of load demand increases and network configuration changes. Index Terms—Active distribution network, multi-agent system, optimal power flow, graph theory.

I. I NTRODUCTION

M

ODERN electric power systems experience a fundamental change from a vertically to a horizontally controlled and operated structure. This transition poses huge challenges for its reliable and economic operation, control and management. Smart Grids have been emerged from various challenging issues of the increasing demand, the transition to sustainable sources, and higher quality requirements. This mainstream research includes a strong interdisciplinary nature by using the state-of-the-art technologies in the fields of Information and Communication Technology (ICT), Power Electronic (PE), and Control System (CS). As a fundamental component of Smart Grids, the future active distribution networks must be able to manage bidirectional power flows, cope with the variability of renewable power generation and adapt to demands of more sophisticated customers. The concept of so-called Active Distribution Network (ADN) has emerged following this requirement to replace the current, passive and less intelligent distribution networks. One of the main expected feature of the ADN is its capability of handling unpredictable power flows through an online, (near) real-time and distributed mechanism. The use of the Optimal Power Flow (OPF) framework is a common practice for handling those problems affecting This work is a part of the project: Electrical Infrastructure of the Future (Elektrische Infrastructuur van de Toekomst, in Dutch), sponsored by the Ministry of Economic Affairs of the Netherlands. The authors are with the Department of Electrical Engineering, Eindhoven University of Technology, 5600MB Eindhoven, the Netherlands (e-mail: [email protected]; [email protected]; [email protected]).

the overall network. It is normally deployed at the dispatch stage to find out the optimal operation of the network with respect to system constraints [1]. Although some distributed OPF techniques have been proposed [2], they need a complex input information and take a relatively long processing time. Recently, a price-based control method, which can also be considered as a distributed OPF solution, was introduced as a reliable solution for systems with high level of DG penetration [3]. By converting the power system parameters into desired market signals, the solution yields nodal prices for generators which help to mitigate the congestion problem and also contribute to other services. The method, however impacts only the generation side of the system in which owners might be influenced by price signals. Other controllable devices of the system, e.g., electronic-based power flow controllers, are treated as passive components. In this paper, we propose a so-called power routing function under the ADN concept to deal with possible network problems prompted by market decisions of consumers and producers and if desired also to support these market actions. The function considers the optimal power flow and solves network congestions through minimization of the production cost, maximization of the transmission reliability, and maximization of serving priority customers, addressed as a minimum cost flow using graph theory. The Scaling Push-Relabel (SPR) algorithm for finding the minimum cost flow are deployed. To comply with the characteristics of the ADN, this algorithms will be implemented in a distributed environment by using Multi-Agent System (MAS) technology. II. P OWER ROUTING F UNCTION A. Active Distribution Network In the ADN, controllable units such as distributed generators, flexible loads, and storage can play an important role in providing ancillary services for network operation [4], [5]. Since the share of DER and RES increases, the participation of DG in ancillary services has been desired to mitigate the effect of system disturbances and system limits [6]. Especially, the increasing number of electronics-interfaced DG units with easier up and down regulation allows DG to participate in frequency and voltage control. ICT services enhance the possibility of sending and receiving information on set points. We assume in this paper that the regulation is flexible enough to allow a coordination between controllable units in coping with disturbances in the ADN. Moreover, there are suitable incentives designed to encourage different entities to participate in providing ancillary services.

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B. Objective function A transition to the ADN comes along with problems related to varying power flows on the network and uncertainty in the forecast of power generation from grid-connected renewable and distributed energy sources. A function of power routing is proposed as an appropriate online solution to manage power flows in real-time. Basically, the function of power routing is the same as the optimization of the power flow which can be formulated in a mathematical model as follows: Minimize: F=

X

+ − − α+ i ∆PGi + αi ∆PGi

i∈G

X 

+




+ − βij ∆PT+ij + βij ∆PT−ij



(i,j)∈T

+

X

γk+ ∆PL+k + γk− ∆PL−k

k∈L

subject to: X

PGi =

i∈G

X

PTij +

X




(1)

PLi

(2)

i∈L

(i,j)∈T

PGmin ≤ PGi ≤ PGmax , ∀i ∈ G i i

(3)

min PD k

(4)

≤ PDk ≤

|PTij | ≤

PTmax , ij

max PD , k

∀k ∈ D

∀(i, j) ∈ T

(5)

where: PGi = PG0 i + ∆PG+i − ∆PG−i , ∀i ∈ G

(6)

PTij = PT0ij + ∆PT+ij − ∆PT−ij , ∀(i, j) ∈ T

(7)

PDk =

0 PD k

+

+ ∆PD k

−

− ∆PD , k

∀k ∈ D

(8)

The objective function F in (1) is the total cost for rerouting power when a disturbance occurs in the system. As the power routing function might change power generation at each + + bus i (∆PGi , ∆PGi ), power flow through each transmission device i − j (∆PT+ij , ∆PT−ij ), and demand at each bus k + − (∆PDk , ∆PDk ) different from the original market clearing 0 0 conditions (PGi , PT0 ij , PDk ), the objective function takes these representative costs into account. Due to the fact that most renewable generation can participate only in down regulation (with cost α− Gi ), integration of storage devices becomes important to give the power routing function flexibility in up regulation (with cost α+ Gi ) of generation. Power flow change on transmission devices influences the total power losses and reliability of the system. However, we consider these transmission costs (βT+ij , βT−ij ) as relatively small compared to the other costs. Since the demand side becomes more active with mechanisms of Demand Side Management (DSM) and Demand Response (DR), its potential in regulating demand + − up (with cost γDk ) and down (with cost γDk ) are considered in the objective function. G, T , and D are respectively generation, transmission, and demand component sets. The power balance condition is presented in the equality constraint (2). The inequality constraints (3) and (4) show the generation and consumption boundaries. The transmitted power needs to be within the device’s thermal limits in the inequality constraint (5). This optimization model assumes that

voltage is autonomously controlled and reactive power is not considered in the formulas. PE devices can facilitate the power routing function in controlling power flows as a so-called smart power router [7]. This combination of an agent and a Power Flow Controller (PFC), for example back-to-back converters [8], can manage autonomous control actions and coordinate with other controllable components. With such control functions, the smart power router is expected to create a flexible environment for the ADN. Installing smart power routers in critical network points (like internet routers) can help to control the power flow actively in order to avoid congestion problems. III. S CALING P USH -R ELABEL ALGORITHM A. Graph model The power system, firstly, is converted to a graph G(V, E), where V presents for the set of vertices (buses in the power system) and E presents for edges (interconnection lines among buses in the power system). A detailed explanation of the conversion from the power system to a directed graph was presented in [7]. In the graph model, the problem in (1) can be defined as a minimum cost flow problem which considers both the shortest path (economy) and the maximum flow (capacity). The SPR algorithm is one of the fundamental solutions to deal with the minimum cost flow. B. Algorithm The SPR algorithm belongs to polynomial-time algorithms to solve the minimum cost flow problem in the graph theory which was explained in detail in [9]. This section summarizes only main steps along with an example to illustrate the algorithm. The algorithm includes two main processes: cost scaling and push-relabel. The cost scaling step determines a boundary for the ǫ-optimal condition in which the scaling factor ǫ is initially set at the maximum cost of the graph, ǫ = max{αi , βij , γk }. The boundary will gradually approach the optimal solution by scaling ǫ ← ǫ/2 after each iterative loop. Within the procedure of cost scaling, the push-relabel step is used. This step aims to push as much excess flow e from a higher node to a lower node. The height of each node is computed from the node potential πi . Since there is an excess in node i (ei < 0) while it is lower than its neighbors, the node will be relabeled (πi = πi + ǫ/2) to be higher than at least one neighbor node to push the excess. Fig. 1 shows an example of a 3-bus system in which the SPR algorithm is illustrated by displaying some first steps. It is assumed in this example that there is no difference between costs for up and down power regulation. Values of parameters of the system are given in Table I. As the source node s is the first active node, it will be relabeled before it pushes flows down to node 1 and 2. When there is no excess at node s anymore, node 2 as the next active node initiates the push-relabel procedure. However, it needs to be relabeled to be higher than at least one neighbor. After relabeling, node 2 can push flow to nodes 1 and 3. A similar step occurs at node 1.

3

Components

DG

0

2

(2,10)

3

(1,5)

0

(1,10)

3

s

(1,5)

1

0

t

0 (6,10)

1

Prosumer agents

(1,5)

Power matching

0 10

0

2

(-1,10)

(1,10)

3

(1,5)

2

(1,0)

(1,5)

10

0

s

0

t

(1,10)

s

3

(-2,5)

t

(3,10)

1

i

(cij, rij)

Fig. 2.

A framework of the agent-based Active Distribution Network

(1,5)

0 ei

Fig. 1.

0

0

(1,5)

1

Energy trading

(-2,5)

0

0 (3,10)

Voltage Regulation

PV

2

Ancillary services

Capacity [MW] max = 10; P min = 0 PG1 G1 max = 10; P min = 0 PG2 G2 max min PD3 = PD3 = 10 max = 5 Pij

Power routing

α+ G1 α+ G2 + γD3 + βij

Generator at bus 1 Generator at bus 2 Load at bus 2 Transmission lines

Cost [pu] = α− =6 G1 = α− =2 G2 − = γD3 =1 − = βji =1

Grid agents

Local control areas

State Estimation

TABLE I D ATA FOR T HE 3- BUS T EST N ETWORK

Push flow on this arc

ej

j

i

Relabel at node i

Illustration of the push-relabel procedure for a 3-bus network

IV. AGENT- BASED IMPLEMENTATION A main focus of the paper is to implement the SPR algorithm in a distributed environment. This section introduces Multi-Agent System (MAS) as a suitable platform for that contribution which can facilitate distributed control and monitoring functions in the ADN. A. Multi-Agent System technology Agent-oriented programming is a relatively new technique to implement artificial intelligence into distributed system operation [10]. An agent can be created by a short program (software entity) to operate autonomously with its environment. Moreover, the agent can interact with each other to form a Multi-Agent System. With characteristics of reactivity, proactiveness, and social ability, the MAS technology can offer numerous benefits in distributed power networks. Actually, a part of the agent’s features has been revealed in some existing applications of the power system before. As an example, an Intelligent Electronic Device (IED) performs various control and protection functions according to changes in their environment, i.e., voltage drop and current increase. Recently, applications of MAS in power systems have been explored in many aspects such as disturbance diagnosis, restoration, protection, power flow and voltage control [11]. Several research projects have begun to investigate MAS as an approach to manage distributed generation, virtual power plants and micro grids [12], [13], [14]. B. Agent-based architecture Fig. 2 shows a possible agent-based ADN structure in which the distributed monitoring and controlled functions are

integrated. In this structure, each grid agent represents a local area network, a part of it or a node. It handles three functional aspects: management, coordination, and execution of actions of the active parties within their areas. These grid agents act as a third party to offer different ancillary services to the Distribution System Operator (DSO) and/or Transmission System Operator (TSO). In the market segment, the so-called prosumer (producer and/or consumer) agents will be active on the energy market and are expected to react to time-varying price signals. Among different power system functions, this paper focuses only on the function of power routing. In this hierarchical architecture, the prosumer agents can update market clearing conditions and use these as input information for different network services provided by the grid agents. The grid agents have authority over other prosumer agents in their areas in the way of monitoring and controlling. These primary (grid) agents act as coordinators of secondary (prosumer) agents to provide set points, give constraints, or get information. They can also communicate their their same level neighbors as in the single layer structure. In addition, it is scalable when they are considered as inferiors of the agents in higher layer. C. Algorithm implementation To implement the SPR algorithm in the distributed agent environment, each normal node i of the graph is represented by a principle agent Ai . The virtual source node is represented by a principle agent As . Since each node needs only knowledge from its immediate neighborhood to execute the algorithm, it suffices that nodes exchange the corresponding information with their neighbors each time that there is a change. Thus each node knows when a branch incident to itself is admissible and can take the corresponding action. Fig. 3 shows a simplified agent sequence diagram of the proposed algorithm. Main kinds of behaviors for each agent are described as follows: • Update information: Principle agent Ai update information from the power network and market conditions. By

4

As

Ai Information()

Aj update()

Information()

update()

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

! i) then

Push(es,Psimax)

Fig. 4. ei = ei + min(es, Psimax)

e>0

If (ei > 0) then [i]

If ( i >

s)

then Push(ei,Pismax)

•

•

i

1 3 5 16 18 20

+ !/2)

Agent sequence diagram for the SPR algorithm

using Inf ormation() messages, they share the information with agent As . If the agent receives a P ush() message, its excess flow ei will be recalculated. If ei > 0, this agent will be added to the list of active nodes S. Cost scaling: The scaling factor ǫ is initial set by the maximum cost of the power network. When the function Cost − Scaling() is called, it transforms ǫ-optimal flow into 1/2ǫ-optimal flow. Relabel node potential: The Relabel() function is called by each agent when it has flow excess and its height is lower than its neighbors. The function adds a value of ǫ/2 to the height. Push flow: When the node agent finds a feasible neighbor in which to push flow, it increases as much as possible the power with respect to its flow excess ei and the connecting arc’s residual capacity Pijmax by calling P ush(). V. S IMULATION

AND RESULTS

In this research, the power grid will be simulated in MATLAB/Simulink while Jave Agent Development Framework (JADE) is used for creating the MAS platform. The protocol for communication between two environments is based on TCP/IP client/server socket communication toolbox. For simplification, DSM and DR are not considered in the simulation + − (γDi = γDo = 0). In addition, the influence of transmission costs on the total operating cost is assumed to be relatively small (βT+ij = βT−ij = 1pu). Simulation is implemented to investigate the performance of the proposed algorithm on a medium voltage (MV) network. This network includes three feeders with ten buses on each feeder, connected to the same substation. The three ends of these feeders are connected through an intelligent node (backto-back converter) to facilitate the power routing function. Parameters of this 32-bus test network are given as follows: • Line section: π-equivalent circuit, line section parameters: Z = 0.25 + j0.178Ω; PTmax ij = 10M W ; • Base load: Each bus has a base load of 1M W + j0.48M V Ar; • Distributed generation: DG units are available at bus 1, 3, and 5 of feeder 1 and 16, 18, and 20 of feeder 2.

Power generaton [MW]

•

Generator

Pgen1

− α+ Gi = αGi [pu] 13 11 9 5 5 5

Pgen2

min PGi [MW] 0 0 0 0 0 0

0 PGi [MW] 13.3 12 12 3 3 3

Pgen3

Pgen4

max PGi [MW] 15 15 15 7 7 7

Pgen5

Pgen6

20 15 10 5 0 0

5

10

15

20 25 30 Simulation time [s]

35

40

45

50

Fig. 5. Change of power generation - Case of network configuration change 20 Power flows [MW]

Fig. 3.

TABLE II I NITIAL S TATE OF T HE R ADIAL T EST N ETWORK

S

If ( i > j) then Push(ei,Pijmax) Relabel ( i =

A single-line diagram of the test network

P23

P56

20 25 30 Simulation time [s]

35

P1112

P1020

10 0 -10 -20 0

Fig. 6.

5

10

15

40

45

50

Change of power flows - Case of network configuration change

Fig. 4 shows a single-line diagram of the test network. The black square symbols present buses having DG units while the remaining buses have only load demand. The simulation starts with an initial state as shown in Table II. The three DG units of feeder 1 provide more power than the other ones connected to feeder 2, but with higher costs. A. Network configuration change In this case, a contingency is considered where line section 5-6 is out of service at t = 1s. The rest of feeder 1 will be supplied by power flows via the intelligent node. At t = 5s, the power routing function detects a new configuration of the network and start its algorithm to give appropriate solution. Fig. 5 shows the change of power generation regarding the change of network configuration. Before applying the power routing function, some power flows violated their thermal capacities due to the change of network configuration, as shown in Fig. 6. This violation is mitigated when DG units are re-dispatch and the intelligent node receives new set points from the power routing function.

5

Total generation cost Total transmission cost

600 400 200 0 0

5

10

15

20 25 30 Simulation time [s]

35

40

45

50

Fig. 7. Change of the the total operating cost - Case of network configuration change

Pgen2

Pgen3

Pgen16

Pgen18

Pgen20

20 Power generation [MW]

Total operating cost [pu]

Pgen1

800

15 10 5 0 -5 0

Fig. 9.

5

10

15

20 25 30 Simulation time [s]

40

45

50

Change of power generation - Case of load demand increase

800 Total operating cost [pu]

35

Total generation cost Total transmission cost

600 400 200 0 0

Fig. 10. increase

5

10

15

20 25 30 Simulation time [s]

35

40

45

50

Change of the the total operating cost - Case of load demand

VI. C ONCLUSION

Fig. 8.

A snapshot of exchanged agent messages in JADE

Fig. 7 summarizes change of the total operating cost when the network configuration changes. The total operating cost increases from 571.6 pu to 644.1 pu due to the change of power flows after disconnecting line 5-6. At t = 15s, either the total transmission cost and the total generation cost starts decreasing. The system is reached a new steady state at t = 25s and the total operating cost is 544.2 pu. Fig. 8 shows some last messages among 32 agents in the JADE’s dialogue. The algorithm takes 1723 exchanged messages to give the results of set points to controllable components of the power grid. In this relative large-scale test network, the communication traffic congestion is still not revealed. B. Load demand increase The case is started by increasing the load demand at bus 21, 25, 26, and 30 of feeder 3 at t = 1s. At t = 5s, each agent starts collecting and sharing information across the MAS platform. At t = 15s, new set points are yielded from the power routing function for DG units. Fig. 9 shows behaviors of DG units before and after receiving new set points. Because of the demand increase, most of generators operate at their maximum capacities while the generation at bus 1 decreases due to its high marginal cost. The total operating cost is increased from 571.6 pu to 591.5 pu during this period. After applying new set points for controllers, the total cost decreases gradually to 463.4 pu. The total transmission cost contributes 25% this cost.

This paper presents a function of power routing as a potential mechanism to manage on-line bi-directional power flow in an Active Distribution Network. The function is based on the distributed environment of a Multi-Agent System platform to implement the Scaling Push-Relabel (SPR) algorithm. Simulation of a medium voltage 32-bus network shows the function’s capability on handling issues of load demand increase and network configuration change. Under this relative large-scale test network, the convergence and robustness of the (SPR) algorithm have been verified. In addition, operation of a number of agents in the simulation gives a positive impression about feasibility of agent technology in support power grid management. This research contributes an important step on establishing a further ICT-based Smart Grids for energy efficiency, flexibility, intelligence, and substantiality. R EFERENCES [1] M. Huneault and F. D. Galiana, “A survey of the optimal power flow literature,” IEEE Transactions on Power Systems, vol. 6, no. 2, pp. 762– 770, 1991. [2] B. H. Kim and R. Baldick, “A comparison of distributed optimal power flow algorithms,” Power Systems, IEEE Transactions on, vol. 15, no. 2, pp. 599–604, 2000. [3] A. Jokic, “Real-time control of power systems using nodal prices,” International Journal of Electrical Power and Energy Systems, vol. 31, no. 9, p. 522, 2009. [4] M. Braun and P. Strauss, “A review on aggregation approaches of controllable distributed energy units in electrical power systems,” International Journal of Distributed Energy Resources, vol. 4, no. 4, pp. 297–319, 2008. [5] S. Chowdhury, S. P. Chowdhury, and P. Crossley, Microgrids and Active Distribution Networks. London, United Kingdom: The Institution of Engineering and Technology, 2009. [6] C. D’Adamo, S. Jupe, and C. Abbey, “Global survey on planning and operation of active distribution networks - Update of CIGRE C6.11 working group activities,” in 20th International Conference and Exhibition on Electricity Distribution, 2009, pp. 1–4.

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[7] P. Nguyen, W. Kling, and J. Myrzik, “An application of the successive shortest path algorithm in multi-agent system based active networks,” European Transaction on Electrical Power, vol. 20, no. 8, pp. 1138– 1152, 2010. [8] R. A. A. de Graaff, J. M. A. Myrzik, W. L. Kling, and J. H. R. Enslin, “Intelligent Nodes in Distribution Systems - Optimizing Steady State Settings,” in IEEE Power Tech, Lausanne, 2007, pp. 391–395. [9] R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network flows: theory, algorithms, and applications. Prentice Hall, 1993. [10] F. L. Bellifemine, G. Caire, and D. Greenwood, Developing Multi-Agent Systems with JADE. John Wiley & Sons, Ltd., 2007. [11] S. D. J. McArthur, E. M. Davidson, V. M. Catterson, A. L. Dimeas, N. D. Hatziargyriou, F. Ponci, and T. Funabashi, “Multi-Agent Systems for Power Engineering Applications; Part I: Concepts, Approaches, and Technical Challenges,” IEEE Transactions on Power Systems, vol. 22, no. 4, pp. 1743–1752, 2007. [12] A. L. Dimeas and N. D. Hatziargyriou, “Operation of a Multiagent System for Microgrid Control,” IEEE Transactions on Power Systems, vol. 20, no. 3, pp. 1447–1455, 2005. [13] R. R. Negenborn, “Multi-Agent Model Predictive Control with Applications to Power Networks,” Ph.D. dissertation, Technische Universiteit Delft, 2007. [14] M. Hommelberg, C. Warmer, I. Kamphuis, J. Kok, and G. Schaeffer, “Distributed Control Concepts using Multi-Agent technology and Automatic Markets: An indispensable feature of smart power grids,” in IEEE Power Engineering Society General Meeting, 2007.

P. H. Nguyen (M’06) was born in Hanoi, Vietnam in 1980. He received the BE degree in electrical engineering at Hanoi University of Technology, Vietnam, in 2002 and the M.Eng. degree in electrical engineering at the Asian Institute of Technology, Thailand, in 2004. From 2004 to 2006 he worked as a researcher at the Power Engineering Consulting Company No. 1, Electricity of Vietnam. From 2006 to 2010 he was a PhD candidate at Eindhoven University of Technology, the Netherlands with ”Electrical Infrastructure of the Future” project. Since the end of 2010 he received his Ph.D. and was employed at the same group as a postdoctoral researcher. His research interests include distributed state estimation, control and operation of the power system, multi-agent system and their applications in the future power delivery system.

Wil L. Kling (M’95) was born in Heesch, the Netherlands in 1950. He received the M.Sc. degree in electrical engineering from the Eindhoven University of Technology, the Netherlands, in 1978. From 1978 to 1983 he worked with Kema and from 1983 to 1998 with Sep. Since then he was with TenneT, the Dutch Transmission System Operator, as senior engineer for network planning and network strategy. Since 1993 he has been a part-time Professor at the Delft University of Technology and since 2000 also a part-time Professor in the Electric Power Systems Group at the Eindhoven University of Technology, the Netherlands. From December 2008 he is appointed as a full-time professor and chair of EES group at the Eindhoven University of Technology. He is leading research programs on distributed generation, integration of wind power, network concepts and reliability. Mr. Kling is involved in scientific organizations such as Cigr and IEEE. He is the Dutch Representative in the Cigr Study Committee C6 Distribution Systems and Dispersed Generation.

Paulo F. Ribeiro (M’78-SM’88-F’03) received his BS in Electrical Engineering from the Universidade Federal de Pernambuco, Recife, Brazil, completed the Electric Power Systems Engineering Course with Power Technologies, Inc. (PTI), and received the Ph.D. from the University of Manchester, Manchester-UK. Presently, he is with Eindhoven University of Technology, the Netherlands. Dr. Ribeiro is active in the IEEE and IEC technical working groups. He is Registered Professional Engineer in the State of Iowa, USA and an European Engineer (EurIng).