Computational Intelligence-Based Demand Response Management in a Microgrid

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Computational Intelligence-Based Demand Response Management in a Microgrid

Pramod Uthpala Herath , Student Member, IEEE, Vito Fusco, Mar´ıa Navarro C´aceres, Ganesh Kumar Venayagamoorthy, Senior Member, IEEE, Stefano Squartini , Senior Member, IEEE,

Francesco Piazza, Member, IEEE, and Juan Manuel Corchado

Abstract—A demand response management (DRM) system is proposed here, in which a service provider determines a mutual optimal solution for the utility and the customers in a microgrid setting. Such a system may find use with a service provider inter- acting with the respective customers and utilities under the exis- tence of some DRM agreements. The service provider is an entity which acts at different levels of the electrical grid and carry out the optimization. The lowest level controls one “neighborhood”

while higher levels of service providers control other lower level service providers. A microgrid consisting of a smart neighborhood of 12 customers was used as experimental case study and an ad- vanced metering infrastructure (AMI) was implemented. Based on the formulation of an optimization problem which exploits price- responsive demand flexibility and the AMI infrastructure, a win- win-win strategy is presented. The interior-point method was used to solve the objective function and the application of particle swarm optimization and artificial immune systems for demand response were explored. Results for a range of typical scenarios were pre- sented to demonstrate the effectiveness of the proposed demand–

response management framework.

Index Terms—Advanced metering infrastructure (AMI), ar- tificial immune system (AIS), demand response (DR), dynamic pricing, multi-objective optimization, particle swarm optimization (PSO), remote management system, smart grid.

Manuscript received April 3, 2017; revised June 24, 2017, August 21, 2017, January 22, 2018, and June 19, 2018; accepted August 16, 2018. Date of publication September 19, 2018; date of current version December 12, 2018. Paper 2017-SECSC-0297.R4, presented at the 2016 Clemson University Power Sys- tems Conference, Clemson, SC, USA, and approved for publication in the IEEE TRANSACTIONS ONINDUSTRYAPPLICATIONSby the Renewable and Sustain- able Energy Conversion Systems Committee of the IEEE Industry Applications Society. This work was supported in part by the National Science Foundation of the United States, under Grants IIP #1312260 and #1408141, in part by the Duke Energy Distinguished Professorship Endowment Fund, and in part by H2020 DREAM-GO Project (Marie Sklodowska-Curie Grant agreement no. 641794). (Corresponding author: Pramod Uthpala Herath.)

P. U. Herath is with the Department of Electrical and Computer Engi- neering, Clemson University, Clemson, SC 29634 USA (e-mail:,pramod.u.

[email protected]).

V. Fusco is with the Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634 USA, and also with the Universit´a Politecnica delle Marche, Ancona 60121, Italy (e-mail:,[email protected]).

M. N. C´aceres and J. M. Corchado are with the University of Salamanca, Salamanca 37007, Spain (e-mail:,[email protected]; [email protected]).

G. K. Venayagamoorthy is with the Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634 USA, and also with the Eskom Centre of Excellence in HVDC Engineering, University of KwaZulu- Natal, Durban 4041, South Africa (e-mail:,[email protected]).

S. Squartini and F. Piazza are with the Universit´a Politecnica delle Marche, Ancona 60121, Italy (e-mail:,[email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TIA.2018.2871390

I. INTRODUCTION

O

NE of the most important concepts for a reliable operation of the electrical grid is the balancing between supply and demand. The requirement however that power generation must meet the energy demand at any time instant is often complicated from the large penetration of intermittent sources of alternative generation. Such an occurrence can result in a significant price volatility that may change from 10–20 cents/kWh during normal periods to more than few US dollars/kWh at peak hours. There- fore, demand response (DR) techniques [1] such as direct load control [2], time-of-use (TOU) pricing, and [3] real-time pricing are used to meet the energy demand and available power, thus improving grid stability to mitigate the negative effects of high price volatility for both the utility and the consumer. Curtail- ment is one such direct load control scheme in which the utility, based on a pre-agreement with the customers, can send requests for load reductions at critical times. Other DR approaches are used to shape the energy demand indirectly by changing the price. Different DR strategies have been studied either for the purposes of load shaping by a utility or aggregator, such that a certain social welfare is optimized [4], or in the context of the design of an appropriate market structure that considers the various mechanisms of demand flexibility [5].

In this paper, the utility is assumed as a price taker with the market price remains unaffected by the demand bids. The elasticity matrix is useful in modeling the effect of the price on the customer’s demand flexibility [6]. Here, the demand is treated as an exponentially decreasing function with the increase in the price of the commodity, followed by calculating the rela- tive slope via linearization around a given point. This value, which is defined as the “elasticity coefficient,” also introduces the matrices of self- and cross-elasticity coefficients to model how the effects of changing the demand of one commodity affect the demand of another. The price elasticity matrix (PEM) has been used to improve the bidding mechanism by account- ing for various forms of demand flexibility [7]. More detailed models of DR have also been developed in which mixed-integer linear programming was used in the optimal scheduling of home appliances and distributed generation systems [8]. Other energy management strategies entail exploring the advantages of intro- ducing the thermal and electric storage in a microgrid scenario [9]. Detailed price responsive customer scenarios have also been used to optimally balance supply and demand via centralized or distributed optimization models to select the optimal schedule

U.S. Government work not protected by U.S. copyright.

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of generation and consumption devices. One such scenario involves dividing the consumers into several classifications and then building a non-linear problem [10]. It assumes knowledge of very detailed information about the price-responsive customers by the utility. Similarly, a modified long run incremental cost pricing model has been developed but must assume detailed customer knowledge or the electric grid [11].

In this paper, for which the basic work is done in [12], the PEM model to influence the price with changing demand is applied. An optimization problem is formulated to minimize the tradeoff between energy procurement costs of the load serving entity and an end-customer’s utility. Assuming the knowledge of the energy demand by forecasting based on historical data, and the knowledge of the day-ahead price at the energy market, an optimal set of prices for the next day can be dispatched to the customers such that the resulting load profile in the next day minimizes the cost of both the energy procurement and the energy cost for the end-users while maintaining the comfort of the customer. Various schemes are available for forecasting [13].

The contributions of this paper are as follows.

1) A viable model based on PEM is proposed for TOU pricing. This introduced pricing model can be used in different environments including hierarchical DR schemes [20]. The DR model has linear constraints and is convex making it easier to solve.

2) Application of different optimization methodologies for DR management is explored.

3) An “SP” model is introduced which would drive the DR scheme and would make sure the various stresses (as studied by many different studies [14], [15]) on the power system by the DR system are managed. Under this con- cept, a neighborhood under the control of an SP is small and the impact on the power system is considered to be minimal. Such an SP service would be viable if the SP op- erates more than one neighborhood, which would reduce the committing costs.

4) An environment consisting of hardware and software solutions for an SP by whom the pricing scheme would be implemented and the residential neighborhood is introduced on which the pricing mechanism is tested on.

The rest of this paper is organized as follows. The modeling and the formulation of the optimization problem are detailed in Section II. Section III describes the solution methods tested.

The implemented microgrid scenario along with the dataset is detailed in Section IV. The experiments carried out and their results with a discussion are presented in Section V. And finally Section VI presents the conclusion.

II. COSTMINIMIZATIONMODEL

A. Modeling of Participants in Microgrid

Customer: the objective function for the customer is the cost functionJ1. Assume thatZ∈R^pandπcR^pare the customer’s energy demand and the price of the energy, respectively:

Z=G+A·x (1)

πc=p0+x (2)

whereG, x, p0R^p×¹andA ∈ R^p^×p, pis the number of the samples per day. The vectorGcontains the day-ahead forecasted values of the hourly customer’s energy demand without doing DR. The vector xis the set of the price differences between p0 andπc, the vector of the energy prices for the customers without doing DR, andπc, which contains their hourly energy prices doing DR.Zis the energy demand subjected to the price changes; the linear dependence fromxis throughA

A=κ

⎛

⎜⎜

⎜⎝

a11 0 .. 0 0 a22 .. 0 .. .. .. ..

0 .. .. app

⎞

⎟⎟

⎟⎠ (3)

where 0 ≤ai,j ≤1, κ≥1. Here, A is the elasticity matrix that defines the flexibility of the customer demand. The diagonal elementκ·aijrepresents the demand change responding to the price change at the same time period. The off-diagonal ele- ments represent the demand change due to the price fluctuations over other time periods. Although various forms of the elasticity matrix are used to analyze the DR method, without affecting the generality of the method,Ais chosen to as diagonal, making it more intuitively reflective of its definition. The objective function for the customers is a cost functionJ1, which can be defined as

J1=Z^T · πc = (G+A·x)^T · (p0+x). (4) To better express the behavior of the customers in response to the changing of the energy price,J1can be extended as

J1= (G+A·x)^T · (p0+x) +αx^T ·A^T ·A·x. (5) The last term of the equation is the comfort cost, a customer with a high value ofαprefers paying more than saving money by changing their energy demand.

Utility: the objective function for the utility is the cost function J2. ConsideringZas the energy demand of the customers, and πu as the set of the energy prices from the energy market,J2

can be written as

J2=βZ^T · πu =β(G+A·x)·π^T_u (6) whereβinRandπuR^p^×1. The energy demand Z, as discussed above, is a linear function of the price differences,x. The term πu is the hourly day-ahead energy price for the utility at the energy market.β, in above equation, is the weighing factor that ensures the balance between the cost functions of the customer and utility.

Constraints: in this optimization problem, the constraints depend on the price fluctuations and the value of the energy demand. These constraints fix the feasible region for the solution of the optimization process: x. For the price fluctuations, the constraint that is on the values ofxis

−λ

⎛

⎜⎜

⎝ 1 1 ..

1

⎞

⎟⎟

⎠≤

⎛

⎜⎜

⎝ x1

x2

..

xp

⎞

⎟⎟

⎠≤λ

⎛

⎜⎜

⎝ 1 1 ..

1

⎞

⎟⎟

⎠ (7)

where λ ∈ R. Theλ coefficient is typically chosen to be a percentage value of the energy price without any DR action. For

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the energy demandZ, the constraints are

G+A·x≥

⎛

⎝0 ..

0

⎞

⎠ (8)

1₋^T ·A·x=0. (9)

Constraint (8) is about the positive value of the energy demand; constraint (9) ensures that the total energy consumption by the consumer remains identical to the forecasted total value without DR, even though the consumption pattern changes.

The high volatility of the price means that the utility wishes to adapt the energy demand to the cost of the energy to optimize their cost. However, the customers tend to optimize the energy management to minimize the bill, depending upon their respective levels of comfort. In this optimization process, based on a deterministic model, the energy procurement for the utility and the bill of the customers are minimized by minimizing the sum of the respective cost functionsJ1andJ2. This process tends to enhance the participation in the DR programs, making the utility less subject to the price volatility, thus increasing both utility profits and benefits for the end-users. The optimization problem can be written as

minimize_x J1(x) +J2(x) subjected to Meq·x =Neq

Mneq·x ≤Nneq. (10) Here, Meq, Neq and Mneq, Nneq are the matrices for the equality and inequality constraints, respectively

Meq =

⎛

⎜⎜

⎝ 1 1 ..

1

⎞

⎟⎟

⎠

T

· A, Neq =0 (11)

Mneq =

⎛

⎜⎝ 1₋

−1₋

−A

⎞

⎟⎠, Nneq =

⎛

⎜⎝ λ·1₋ λ·1₋ G

⎞

⎟⎠. (12)

Mneq andNneq are used in (10) to express the inequality constraints (7) and (8). The equality constraints (9) have been defined in (10) throughMneq andNneq. This problem in (10) is quadratic with linear constraints and efficient solvers based on interior-point methods could be used. The solution is the vector x ∈ R^p×1, which is used to compute the prices published by the utility for the individual customers the day ahead, such that the resulting load profile in the next day minimizes the costs of energy procurement as well as the energy bill of the customer with a minimum compromise in comfort.

III. SOLUTIONMETHODS

In this section, the interior-point method, constrained particle swarm optimization (PSO), and artificial immune system (AIS) algorithms are explored as possible algorithms to be applied for complex DR models. In the following sections, each of these methods is described.

A. Interior-Point Method

The interior-point method is a popular method utilized in mathematical optimization. The interior-point method is best for solving this problem as the objective function is a convex differentiable function with linear constraints. The MAT- LAB optimization toolbox is used for the implementation of the interior-point method. In this study, this implementation was utilized to calculate the solution, the results of which are shown in Figs. 4–8.

B. Constrained PSO Method

First proposed by Kennedy and Eberhart [16], PSO is a nature- inspired algorithm similar to the genetic algorithm, which although simpler and less computationally intensive than the genetic algorithm is nonetheless effective. PSO involves a “swarm of particles” swimming through the solution space searching for the best solution, the movement of which is determined by a set of equations. Although PSO in its original form can only handle unconstrained problems, several adaptations have been proposed for constrained problems.

The authors base their approach upon their established procedure in which three equations governing the movement of the particles [17]. Either of the first two equations govern the “velocity” of each of the particle depending on whether that particle is currently in the feasible region. If the particle is in the feasible region, the equation for velocity is

Vid,k+1=wVid,k +c1rand1(Xpb estid,k−Xid,k) +c2rand2(Xgb estd,k−Xid,k). (13) Here,Vid,k+1 is the velocity of the dth dimension of the ith particle in the next (k+1st)iteration,wis the inertia of the particle,Vid,k is the velocity of the dth dimension of the ith particle in the current (kth) iteration.c1is the cognitive acceleration constant,c2is the social acceleration constant,Xpb estid,k

is the position of the particle in the dth dimension, andc1 and c2are random numbers. In this scenario, the particle makes use of both the best solution found by the whole system and the best solution found by itself thus far to determine the next set of arguments used to check the solution. However, if the particle has yet to determine a feasible solution, any influence of an infeasible solution is of no use. Therefore, the velocity for the next iteration for a particle without any feasible solution is defined as

Vid,k+1=wVid,k+ (c1+c2)×rand×(Xgb estd,k−Xid,k). (14) Using the velocity calculated, the next position in the search space means that the particle calculating the objective value is expressed as

Xid,k+1=Xid,k +Vid,k. (15) The results obtained by PSO on the model described in this paper are illustrated in Fig. 9.

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C. AIS Method

Bioinspired algorithms (e.g., artificial neural networks, genetic algorithms, and swarm intelligence) are widely used to solve different research problems, following biological principles and behaviors [18].

Following this natural process, de Castro and Timmis [19]

developed the CLONALG algorithm, a clonal selection procedure for use in pattern recognition. The algorithm suppresses, reproduces, and mutates the individuals according to the affinity with an external antigen. The new individuals are immersed in the population for evaluation, reproduction, mutation, and suppression according to their affinity. Each operation is condi- tioned by the prior creation of parameters to influence the final results of the mutation, suppression, or reproduction processes, respectively.

Specifically, in the opt-aiNet formation, developed to optimize such problems via the principles of the CLONALG, the antigens are configured to represent the information recognized by the antibodies [19]. Specifically, this affinity is formalized with a fitness function to optimize so that the higher values of fitness function correspond to a high affinity between the antibodies and antigens.

Initially, a certain numberNof antibodies are randomly created and introduced to the antigens to measure their affinity between each antigen and antibody. An optimization function is used to calculate this affinity. Those antibodies with high affinity are selected and a number of clonesNc are generated for purposes of mutating each antibody, a process that is controlled according to the affinity values. Therefore, the high values in the fitness function mean a low mutation process and vice versa.

Here,δis lineally combined to produce a new individual, according to

δ =e^fⁱ

β (16)

whereβ is a constant that is empirically obtained to normalize the effect of the fitness ratefiobtained by each cell.

Likewise, those antibodies with fitness values below a given thresholdtsare suppressed from the total population. This process is repeated until the solution is achieved and the population converges in one point. The algorithm (Algorithm 1) is summa- rized as follows.

The results of the algorithm on the system are shown in Figs. 10–13.

D. Discussion of Algorithms

While the interior-point method is suitable for this convex optimization problem, the PSO and AIS methods are better at solving more complex, non-linear and non-convex objective functions which arise in more complex DR systems.

IV. MICROGRIDENVIRONMENT

A. SP

In this section, an “SP” model is introduced for use in the application of the DR scheme. The SP resides between the

Algorithm 1: Artificial Immune System.

Procedure AIS (problem) Set parametersN,Nc,ts,β

Initialize population with random values For each iteration, do:

a. Calculate Fitness for each antibody

b. Reproduce the antibodies with high affinity values c. Mutate the clones of each antibody according to

their affinity values

d. Suppress individuals with low affinity values e. Generate random individuals and insert them in

the population

Repeat until convergence criterion is met

customer and utility and provides the DR service. The action is accomplished via interactions with the customers and the utility via the use of suitable existing pre-agreements between each.

Using the customer flexibility and comfort and the interest of utility as the criteria, an optimal set of hourly day-ahead prices πcis selected for the customers to minimize the cost of energy procurement. Thus, this scheme serves both the interests of the utility and the customers. The conceptual method adopted is shown in Fig. 2 and the software components used by the SP are described below.

The SP is a hierarchical entity as shown in Fig. 1(b). Different levels of SPs are positioned at various levels of the transmission and distribution lines and would control the SPs below. The super SPs will take care of a part of the grid, making sure the SPs below would not violate any grid constraints while carrying out DR. At the very lowest level, the local SP will interact with the customers through advanced metering infrastructure, and would make sure the correct pricing is offered for the customer.

The intent here is to address the lowest level of SPs.

V. RESULTS ANDDISCUSSION

The microgrid scenario described in the previous section is used as the test bed for testing the proposed DR strategy. The authors detail, in five case studies, the exploitation of price-based DR mechanisms and the optimization of the energy procurement process for the utility, illustrating how changes in both customer comfort and flexibility affect the mutual benefits. The values of the optimization problem parameters used for each case study (see Table I) include both customer flexibility and the comfort level.

The day-ahead price of the energy procurement, which is from the ISO New England website, is related to the first week of August 2015 in Vermont. The customers outside the DR strategy are assigned a given fix price while those inside the DR are subjected to the day-ahead price chosen by the provider as determined by the optimization process.

The forecasted energy demand of the neighborhood, which exhibits two trends during the midweek and the weekend, is obtained by modeling the energy consumption of each neighborhood customer. For example, the houses with students tend to have higher energy demands during the evening with respect

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Fig. 1. (a) Conceptual diagram for local SP. (b) Hierarchical structure of SPs.

Fig. 2. System diagram.

TABLE I CASESTUDIES

Fig. 3. Energy prices.

to those who are retirees. To show the benefits of the DR strategy for both the customers and the utility, the following formula is used to compute the percentage cost variation:

Costf −Costi

Costi ·100 (17) whereCostiis the cost of the energy procurement for the utility, or the cost of the bill for the customers without DR.Costfis the same cost, but when they take part in the DR programs.Costi

for the customer represents payment for energy at a reasonably fixed price of 14.12 cents/kWh. For the utility, it is the hourly day-ahead prices for the energy demand at that time interval.

The following sections discuss the observations of the exper- iment in detail.

A. Sensitivity to Flexibility

As defined in (5) and (6), the cost functionsJ1andJ2depend on flexibility through the elasticity matrixA. After assigning an appropriate value ofκ, matrixAis used to obtain the variation of the customer’s hourly flexibility by changing the values of the diagonal coefficients (see Table I). The customer flexibility is increased through Cases I–III to optimize the cost of the energy procurement and energy consumption for the customers and utility (see Figs. 4–6).

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Fig. 4. Case I simulation results. (a) Percentage cost variation. (b) Energy demand.

Fig. 5. Case II simulation results. (a) Percentage cost variation. (b) Energy demand.

In Case I, the flexibility of the customers is very limited during the breakfast, lunch, and dinner hours with of 0.07, 0.1, and 0.03, respectively. The DR is applied more during the afternoon and night, where the values of the flexibility increase to around 0.2. In Cases II and III, the flexibility doubles to a value of approximately 2.8 times with respect to the first case. As expected, the greater the level of customer flexibility, the greater the ben- efit of DR to both parties. Indeed in Case III for the customers, the percentage cost variation decreases from a weekly average

Fig. 6. Case III simulation results. (a) Percentage cost variation. (b) Energy demand.

of approximately –2.3% to –4.2% and then to –5.7%, and for the utility from –2.4$% to –4.5% and then to –6.5%.

B. Sensitivity to Comfort Preference

The parameterαexpresses a weight to the comfort term of the cost functionJ1as defined in (5). An increase in the value of α makes it possible to model the customer’s inertia that characterizes a change in the energy demand, which depends upon a change in the energy price.

The results of the analysis of the comfort dependence, as described in Cases II, IV, and V are shown in Figs. 5, 7, and 8.

Indeed, assigningαthe values of 0.1, 30, and 60 increases the weight of the comfort terms in (5), thus reducing the influence of dynamic pricing upon customer energy demands.

The percentage cost variation changes around the average values of –4.2%, –1.7%, and –0.8% for the customers, and of –4.5%, –2.5%, and –1.4% for the utility in case studies II, IV, and V, respectively. In summary, higher comfort levels lead to lesser cost savings for both sides. However, this penalizes the customers more than the utility.

C. Sensitivity to Dynamic Pricing

As observed, customers tend to increase the energy demand when the price is low and reduce it when the price is high as expected. As defined in the constraints of the optimization problem (10), however, the energy demand remains the same. The price of the energy procurement, in the first week of December 2015 in Vermont, oscillates from approximately 1 to 5 cents/kWh.

Here, the choice of the day-ahead prices for the customer provides an incentive to purchase the energy when the cost for the utility is less. As shown in Fig. 3, the choice of the day-ahead

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Fig. 7. Case IV simulation results. (a) Percentage cost variation. (b) Energy demand.

Fig. 8. Case V simulation results. (a) Percentage cost variation. (b) Energy demand.

prices for the customers serves as an incentive to purchase the energy when the utility cost is lower.

D. Stochastic Optimization Algorithms

The results generated with the stochastic optimization algorithms (PSO and AIS) are comparable to that generated with the interior-point method. The superiority of these algorithms is evident when they are applied to complex DR scenarios that are characterized by an inherently complex objective function.

Fig. 9. Case I simulation results using PSO. (a) Percentage cost variation.

(b) Energy demand.

Fig. 10. Case I percentage cost variation using AIS.

Fig. 11. Case II energy demand using AIS.

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Fig. 12. Case III simulation results using AIS. (a) Percentage cost variation.

(b) Energy demand.

Fig. 13. Case V percentage cost variation using AIS.

VI. CONCLUSION

The application of DR techniques provides a number of po- tential benefits for balancing the energy problems of the grid.

In this paper, a simple, but effective DR method is described.

The authors show that the simple exploitation of a management framework for the DR can reduce the exposure of the utility to the market volatility while improving costs for both the utility and customers, avoiding the need of complex technological solutions. The interior-point method was used to solve the optimization problem, followed by an exploration of the possible usage of both PSO and AISs.

This proposed management system, which is also in compli- ance to the IP technologies, exploits a modern and widely used practical technology for easy adaptation into the power environment to create real-world applications that avoids the expensive and invasive use of complex technology solutions.

Future work will focus on the integration of the renewable sources in the problem formulation and modeling the energy behavior of the customer.

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Informat., vol. 13, no. 4, pp. 1806–1816, Aug. 2017.

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Pramod Uthpala Herath (S’16) received the bach- elor’s degree specializing in computer engineering from the University of Peradeniya, Peradeniya, Sri Lanka, in 2012, and is currently working toward the Ph.D. degree in computer engineering in the Hol- combe Department of Electrical and Computer Engi- neering, Clemson University, Clemson, SC, USA.

He was a Lecturer for three years. He is a Research Assistant with the Real-Time Power and Intelligent Systems Laboratory. His current research interests include smart grid, demand response and parallel and high-performance computing.

Vito Fusco was born in Foggia, Italy, on April 1991.

He received the master’s degree (with hons.) in electronic engineering from the Universit`a Politecnica delle Marche of Ancona, Ancona, Italy, in 2015.

He joined the Real-Time Power and Intelligent Systems Laboratory, Clemson University, Clemson, SC, USA, as a Visiting Student in 2015, for a smart grid research project. Since 2016, he is working in Bari, Italy, as a Power Systems Engineer at Enel In- frastructures and Networks Italia, where his main ac- tivities involve smart grid deployment projects.

Mar´ıa Navarro C´aceres (S’16) received the Ph.D.

degree in computer engineering from the University of Salamanca, Salamanca, Spain, in 2018.

She is currently with the University of Salamanca as an Associate Professor. She has participated in energy efficiency projects, such as the DREAMGO project or the IOTEC project, among others. Her research interests include bioinspired algorithms and specifically on analysis of energy characteristics and optimization from artificial immune systems.

Ganesh Kumar Venayagamoorthy (S’91–M’97–

SM’02) received the Ph.D. degree in electrical engineering from the University of Natal, Durban, South Africa, in February 2002, and the M.B.A. degree in entrepreneurship and innovation from Clemson Uni- versity, Clemson, SC, USA, in 2016.

He is the Duke Energy Distinguished Professor of power engineering and a Professor of electrical and computer engineering and automotive engineering with Clemson University. Prior to that, he was a Professor of electrical and computer engineering with the Missouri University of Science and Technology, Rolla, MO, USA, from 2002 to 2011. He is the Founder (2004) and Director of the Real-Time Power and Intelligent Systems Laboratory. He holds an Honorary Professor position in the School of Engineering at the University of Kwazulu-Natal, Durban, South Africa. He has authored or coauthored more than 500 refereed technical articles. His publications are cited∼15 000 times with an H-index of 60. He has been involved in more than 70 sponsored projects in excess of US $10 million. He has given more than 300 invited keynotes, plenaries, presentations, tutorials, and lectures in more than 40 countries to date. His research interests are in the research, development, and innovation of smart grid technologies and operations, including intelligent sensing and monitoring, intelligent systems, integration of renewable energy sources, power system optimization, stability and control, and signal processing.

Dr. Venayagamoorthy has been involved in the leadership and organization of many conferences including the General Chair of the Annual Power System Conference (Clemson) since 2013, and the Pioneer and Chair/Co-Chair of the IEEE Symposium of Computational Intelligence Applications in Smart Grid since 2011. He is currently the Chair of the IEEE Power and Energy Society Working Group on Intelligent Control Systems, and the Founder and Chair of IEEE Computational Intelligence Society Task Force on Smart Grid. He has served as Editor/Guest Editor of several IEEE transactions and Elsevier jour- nals. He is a Fellow of the Institution of Engineering and Technology, U.K., and the South African Institute of Electrical Engineers.

Stefano Squartini (SM’12) was born in Ancona, Italy, on March 1976. He received the Italian Laurea (with hons.) in electronic engineering and the Ph.D.

degree from the University of Ancona (now Polytech- nic University of Marche, UnivPM), Ancona, Italy, in 2002 and November 2005, respectively.

He was Post-doctoral Researcher with UnivPM from June 2006 to November 2007, when he joined the Department of Information Engineering as an Assistant Professor in electrical circuit theory. He has been an Associate Professor with UnivPM since November 2014. He is author and coauthor of many international scientific peer-reviewed articles (more than 180). His current research interests include computational intelligence and digital signal processing, with special focus on speech/audio/music processing and energy management.

Dr. Squartini is an Associate Editor for the IEEE TRANSACTIONS ONNEURAL NETWORKS ANDLEARNINGSYSTEMS, IEEE TRANSACTIONS ONCYBERNETICS, and IEEE TRANSACTIONS ONEMERGINGTOPICS INCOMPUTATIONALINTELLI- GENCE. He is a Member of IEEE Computational Intelligence Society. He joined the Organizing and the Technical Program Committees of more than 70 international conferences and workshops in the recent past.

Francesco Piazza (M’87) was born in Jesi, Italy, on February 1957. He received the Italian Laurea (with hons.) in electronic engineering from the University of Ancona, Ancona, Italy, in 1981.

From 1981 to 1983, he was involved in image processing with the Physics Department. In 1983, he was with the Olivetti OSAI Software Development Center, Ivrea, Italy. In 1985, he joined the Depart- ment of Electronics and Automatics, University of Ancona. He is currently a Full Professor of electrical science with the Universit`a Politecnica delle Marche, Ancona. He is author or coauthor of more than 300 international papers. His current research interests include circuit theory and digital signal processing including adaptive DSP algorithms and circuits, artificial neural networks, and speech and audio processing.

Juan Manuel Corchado received the Ph.D. degree in computer science from the University of Sala- manca, Spain in 1998 and in artificial intelligence from the University of Glasgow, UK, in 2000. He is a Full Professor with Chair at the University of Sala- manca, Salamanca, Spain. He is the Director of the Bioinformatics, Intelligent Systems and Educational Technology Research Group, which he created in the year 2000. He also oversees the Master’s programs in Internet of Things, security, mobile technology, community management and management for TIC enterprises at the University of Salamanca. His research is specialized in smart cities, power systems, and intelligent agents. He has collaborated more than 20 international projects, has led more than 10 thesis, and has participated in more than 50 publications related to power systems.

Mr. Corchado is also the President of the IEEE Systems, Man and Cyber- netics Spanish Chapter, Academic Director of the Institute of Digital Art and Animation of the University of Salamanca.