Inter national J our nal of Electrical and Computer Engineering (IJECE) V ol. 15, No. 6, December 2025, pp. 5106 ∼ 5118 ISSN: 2088-8708, DOI: 10.11591/ijece.v15i6.pp5106-5118 ❒ 5106 SGcoSim: a co-simulation framew ork to explor e smart grid applications Abdalkarim A wad 1 , Abdallatif Ab u-Issa 1 , P eter Bazan 2 , Reinhard German 2 1 F aculty of Engineering and T echnology , Birzeit Uni v ersity , Birzeit, P alestine 2 Department of Computer Science, Computer Netw orks and Communication Systems, Uni v ersity of Erlangen, Erlangen, German y Article Inf o Article history: Recei v ed Aug 26, 2024 Re vised Jul 21, 2025 Accepted Sep 14, 2025 K eyw ords: Co-simulation Demand response Smart grid V olt/V AR optimization W ide area monitoring ABSTRA CT Under the smart grid concept, ne w no v el applications are emer ging. These applications mak e use of inform ation and communication technology (ICT) to help the electrical grid run more smoothly . This paper introduces SGcoSim, a co-simulation frame w ork that inte grates po wer system modeling and data communication to enhance smart grid applicat ions. The frame w ork utilizes OpenDSS for simulating po wer distrib ution components and OMNeT++ for communication modeling, enabli ng real-time peer -to-peer interactions via wireless sensor netw ork (WSN) techniques. V irtual cord protocol (VCP) is deplo yed for ef cient routing and data management within the eld area netw ork. SGcoSim’ s functionality is demonstrated through tw o case studies: a phasor measurement unit (PMU)-based wide-area monitoring system and an inte grated v olt/V AR optimization with demand response (IVV O-DR) application. Results indicate signi cant reductions in ener gy consumption and po wer losses, highlighting the capabilities of SGcoSim. This is an open access article under the CC BY -SA license . Corresponding A uthor: Abdalkarim A w ad F aculty of Engineering and T echnology , Birzeit Uni v ersity Al-Marj str . 1, Birzeit, P alestine Email: akarim@birzeit.edu 1. INTR ODUCTION T o impro v e po wer netw orks, the smart grid inte grates a signicant number of elements such as distrib uted generators, communication technologies, computer and intelligence, sensing, and control. Ne w applications such as smart metering infrastructure, demand response, and inte gration of distrib uted ener gy resources ha v e e v olv ed as a result of the smart gri d. Emer ging smart grid applications ha v e the potential to optimize the operation of the po wer netw ork, resulting in a reduction in ener gy demand. Using phasor measuring units (PMUs) to perform real-time wide-area monitoring is a crucial strate gy to respond quickly to netw ork changes and a v oid major problems such as po wer outages and damage. The basic purpose of V olt/V AR optimization is to run the v arious V olt/V AR control de vices as ef ciently as possible in order to sa v e ener gy and k eep the v oltage within acceptable limits. There are tw o components of V olt /V AR control: V olt control and V AR control. T o reduce po wer usage, the V olt control emplo ys the conserv ation v oltage reduction (CVR) idea. It lo wers the v oltage at the end user , which sa v es po wer usage. It is assumed that when the v oltage is lo w , the de vices will consume less po wer . The v oltage at the load tap changer is controlled by CVR. V AR control attempts to reduce po wer losses by injecting or absorbing reacti v e po wer . Capacitor banks were once the only w ay to add or remo v e reacti v e po wer . In v erters and other po wer electronics ca n no w be utilized to inject or absorb reacti v e po wer J ournal homepage: http://ijece .iaescor e .com Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 5107 from PV systems and storage elements. In this article, it is assumed t hat the syste m includes capaci tor banks, photo v oltaics (PVs), and storage de vices that can inject or absorb reacti v e po wer . Demand response (DR) is one method to reduce demand during periods of e xcessi v e demand. It shi fts a portion of the load from high-demand to lo w-demand periods. The periods of high and lo w demand are not constant. On a w orking day , for e xample, the early e v ening and early morning can be high-demand periods. This will eliminate the need for corporations to b uild ne w po wer plants to meet peak demand. In recent years, there has been a lot of studies done on the issue of inte grating po wer systems and information and communication technology (ICT) simulators, with the majority of the solutions concentrating on the usage of specic smart grid principles in distri b ut ion systems, such as distri b ut ed generation, aggre g ated loads, and microgrids. Most of these co-simulators used predened delays to model data communication netw orks, and it is crucial for delay-sensiti v e applications that co-simulators truly replicate the complete netw ork stack. Additionally , there are not man y co-simulators that i ncorporate sophisticated optimization techniques inside the co-simulator . The w ork presented in [1] focuses on protection approaches and wide-area measurements and control. It pro vides a simple approach to simulate data communication netw orks. In [2] the inte gration of OMNeT++ with Po werF actory , a commercial po wer system analysis softw are, is detailed. In [3] the co-simulation frame w ork couples open distrib ution system simulator (OpenDSS) with OMNeT++, using the h yperte xt transfer protocol for the data e xchange between the tw o components. Because of the importance of comm unication in smart grids, the com bination with OMNeT++ is also planned for modular simulation of comple x systems (MOSAIK) [4], [5]. The paper [6] presents methods to e v aluate critical lines and nodes in c yber -ph ysical po wer systems (CPPS) from three perspecti v es: netw ork information, properties, and structure. The authors in [7] ha v e introduced a h ybrid synchronization scheme using synchrophasors and generic object-oriented substation e v ent (GOOSE) messages for rapid and automatic reconnection in po wer systems. It le v erages direct PMU communication to reduce latenc y and costs, coordinating A VR and turbine go v ernor control. Real-time simulations v alidate the method’ s ef fecti v eness and interoperability . A nother CPPS testbed has been presented in [8] that emplo ys co-simulation to analyze optimal po wer o w (OPF) s trate gies with di strib uted ener gy resources (DERs). It dynamically optimizes po wer netw ork losses and operational costs , adapting to v arying DER penetration le v els. Experiments on an IEEE 39-b us system conrm that the approach enhances grid stability and ef cienc y . The w ork in [9] presents a co-simulation frame w ork inte grating real-time simulators (real-time digital simulators (R TDS), T yphoon, OP AL real-time (OpalR T)) and netw ork simulator (NetSim) to e v aluate smart grid communication performance. It e xamines throughput, delay , and jitter in pri v ate and public netw ork scenarios using a Conseil International des Grands R ´ eseaux ?lectriques (CIGRE) benchmark system. Results highlight stable throughput b ut increased delay and jitter in public netw orks, underscoring pri v ate netw orks ’ suitability for delay-sensiti v e smart grid operations. Some frame w orks ha v e been proposed to study c yber -attacks [10]– [12]. An o v ervie w of some co-simulation frame w orks for smart grid analysis is gi v en in [13]. In general, these frame w orks lack the inte gration of e xplicit optimization tools and do not pro vide the capability to e xplore techniques deri v ed from wireless sensor netw orks. The proposed frame w ork, SGcoSim, mak es it possible to e xplore approaches that require optimization. Additionally , it allo ws testing approaches from W ireless sensor netw orks in the eld of smart grid. 2. SGCOSIM In this section we introduce SGcoSim frame w ork, which is an e xtension of SGsim [14]. T w o types of netw orks should be considered when dealing with smart grid a pp l ications, namely the electricity netw ork and the communication net w ork. The electric ity netw ork b ui lds the e x i sting components of the po wer grid such as loads, DER and storage. OpenDSS [15] is chosen to simulate the electricity netw ork while OMNeT++ [16] is emplo yed to simulate the data communication netw ork. In the pre vious implementation we emplo yed component object model (COM) to enable the comm un i cation between OMNeT++ and OpenDSS. COM is a binary-interf ace standard that allo ws dif ferent softw are components to communicate and interact, re g ardless of the programming language used to create them. It enables code reuse and modular design as well as inter - process communication (IPC). In the updated implementation, in addition to COM serv er , we emplo yed object linking and embedding (OLE) to perform the communication between the tw o simulators. OLE is b uilt on top of COM and allo ws SGcoSim: a co-simulation fr ame work to e xplor e smart grid applications (Abdalkarim A wad) Evaluation Warning : The document was created with Spire.PDF for Python.
5108 ❒ ISSN: 2088-8708 embedding and linking to documents and objects between applications. This w ay , we do not need a dynamic- link library (DLL) to enable the communication and therefore it is easier to install. INET Frame w ork [17] is used to b uild the data communication netw ork. It has well-tuned components such as TCP/IP , Ethernet and 802.11. The f act that the nodes in the po wer grid are almost static mak es the smart grid a potential application for WSNs. W e adopted approaches from WSN for routing and data management in the grid. W e used VCP for routing and data management inside the electricity netw ork. VCP is a lightweight and scalable routing protocol that Constructs a virtual linear topology (a “cord”) o v er a distrib uted netw ork. It assigns each node a unique virtual coordinate or position along this cord. Additionally , it uses these coordinates for ef cient routing, neighbor disco v ery , and resource location. 2.1. Electricity netw ork The electricity netw ork should be supplied as a script to the simulator . It contains the dif ferent components and the interconnections between these components (topology). In addition to con v entional po wer grid components such as cables and transformers, OpenDSS has the ability to simulate se v eral types of loads, supplies, and storage sys tems. It pro vides se v eral models for the load such as constant impedance, constant P and Q, and ZIP load models. Moreo v er it pro vides simulation models for rene w able ener gy sources such as a PVs. OpenDSS allo ws dif ferent solution modes from v ery lo w t ime step size (micro seconds), that is required to capture the transient signals, to yearly simulation e xperiments. 2.2. Data communication netw ork The nodes in the netw ork are capable of data look-up, routing and storing. An y component of the po wer grid (e.g., a House) is equipped with a wireless node which enables it to communicate with other nodes. Furthermore, it is possible to place nodes inside the netw ork to insure netw ork connecti vity . This mak es it possible to b uild a relati v ely cost-ef fecti v e eld area netw ork o wned by the electricity compan y . This w ay , electricity distrib ution companies can install smart grid applications to enhance the operation of the po wer grid. VCP [18], [19] is a distrib uted has h table (DHT)-based routing and data management protocol for WSN. It combines data look-up and routing in a protocol that of fers peer -to-peer comm unication. VCP maintains a virtual cord that connects all nodes in the netw ork and allo ws data pieces to be inserted into sensor nodes and retrie v ed. Using the put command, the Controller can store its position inside the netw ork. This w ay , other nodes can retrie v e this information using the get command. All nodes use the same hash function to map data into the cord. F or instance, if the hash v alue of Controller is 0.41, then the data corresponding to the controller should be stored at node 0.43 which is the succeeding node of 0.41. No w , if another node needs this information, then it uses the same hash function to retrie v e the required information (e.g., House6 needs the position of Controller ). 2.3. Simulator components The simulator components consist of: a. Po wer grid model: A scr ipt feeds OpenDSS with information on the v arious components of the po wer grid and their interconnections. It includes transmission lines, transformers, generators, and loads. b . Load (OpenDSS side): A te xt le contains the load v alue at dif ferent time steps. c. Load (OMNeT++ side): A program that represents the beha vior of the load (e.g., House). It is possible to connect/disconnect, scale up/do wn, or change the po wer f actor of the load at run-time. d. Supply (OpenDSS side): This le pro vides a time series of the production of a DER. e. Supply (OMNeT++ side): A program that represents the beha vior of a supply (e.g., PV). Similar to the load component, this component mak es it possible to connect/disconnect, scale up/do wn, or change the po wer f actor of the supply at run-time. f. SGSimInterf ace: it enables the communication between OpenDSS and OMNeT++. g. Solv er: This component synchronizes the operation between the po wer simulator (i.e., OpenDSS) and the data communication simulator (i.e., OMNET++). The communication between the simulators is done using COM and OLE interf ace. h. De vice: It represents po wer grid de vices (e.g., ba tteries, switches, and capacitor banks). This component can be controlled o v er the COM interf ace. j. Sensor: It collects data from a single component (e.g., b us, load, or DER) and sends it to other components. F or e xample, the phasor measurement unit (PM U) is a sensor that uses simulated TCP/IP pack ets to send Int J Elec & Comp Eng, V ol. 15, No. 6, December 2025: 5106-5118 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 5109 data to the PDC interf ace. The data is formatted according to a standard (e.g., IEEE c37.118) so that real components (e.g., phasor data concentrator (PDC)) can recei v e the pack ets. k. Controller: This component controls the operation of dif ferent units wit h i n the grid. It changes the set points of these units by solving an optimization problem. It sends an xml le that describes the grid to a solv er and then recei v es the ne w set point s and then adapts these set points. It controls the v oltage, acti v e and reacti v e po wer of elements such as PV , battery and On load tap changer (OL T). l. PDC interf ace: This element is an interf ace between the simulator and a real measurement unit such as OpenPDC [20]. F or such an application, the simulation should be run using a real-time mode. 2.4. Phasor measur ement units A phasor measurement unit (PMU) is a de vice that measures po wer system-related quantities at a high rate (e.g., 120 times per second). It determines the amplitude and angle of a po wer quantity lik e v oltage or current. It also monitors frequenc y , temperature, and other f actors. A high-precision timer is used to stamp the readings. GPS is commonly utilized to produce a preci se time stamp for this purpose. The measured v alues are encoded using a standard (for e xample, IEEE c37.118) and then communicated across data communication netw orks. 2.5. A phasor data concentrator A phasor data concentrator (PDC) g athers information from a v ariety of sources, including PMUs and other PDCs. It creates a system-wide measurement set by correlating phasor data by time-tag. As a result, it is critical t o stamp the reading wi th a precise time. PDCs e xamine the phasor data for v arious quality issues and add rele v ant ags to the link ed data stream. It looks f o r disturbance ags and sa v es data les for later e xamination. It also k eeps track of the total measurement system and displays and records the results. A direct connection to a SCAD A or an ener gy management system (EMS), which can be used to monitor and re gulate things lik e electricity , is one of the special outputs a v ailable. The open-source phasor data concentrator (openPDC) is a system for managing, processing, and responding to f ast-changing phasor data streams. The openPDC, in particular , is capable of handling an y sort of data that can be described as time-stamped measured v alues. These measured v alues are simply quantitati v e amounts acquired at a source de vice. The y are also kno wn as points, signals, e v ents, time-series v alues, or measurements. Measurement types include frequenc y , current, and v oltage. W ith the help of additional sensors, we can measure temperature and humidity . A precise time stamp is tak en when a v alue is measured, commonly using a GPS clock. The v alue is then streamed to the openPDC, where it can be time-aligned with other incoming measurements, allo wing an action to be tak en on a lar ge slice of data that w as all measured at the same time. 2.6. IEEE c37.118 In SGcoSim, the IEEE c37.118 standard has been implemented. The standard describes ho w synchronized phasors in po wer systems should be measured. It contains both a method for quantifying the measurement and tests to conrm that it is accurate. A data transmission protocol is also established. There are v arious message formats for transmitting this data in a real-time system. Synchrophasor measurements must be precisely synced to UTC time. The system must be able t o recei v e time from a highly reliable source, such as the global positioning system (GPS) , according to the specicat ion. All message frames be gin with a 2-byte SYNC w ord (0xAA and 8 bits that indicate the frame type), follo wed by a 2-byte FRAMESIZE w ord, and a 2-byte IDCODe w ord. A timestamp made up of a four -byte second of the century . A check w ord (CHK), which is a CRC-CCITT ends each frame. In this CRC-CCITT , the generating polynomial X 16 + X 12 + X 5 + 1 with a starting v alue of (he x FFFF) is emplo yed. SYNC is an acron ym for ”synchronization. ” The rst w ord is sent rst, follo wed by the check w ord. Phasor and frequenc y v alues can be transmitted in either a 16-bit inte ger or a 32-bit oating-point format. Implementing this standard allo ws e xisting softw are, such as the phaser data concentrator , to be inte grated. Figure 1 displays the data that OpenPDC recei v es from a PMU. It also enables the e xploration of methodologies that require real-time data. 2.7. Optimization tools One of the smart grid’ s primary goals is that it mak es the operation among dif ferent entities more ef cient. This goal can be achie v ed using optimization techniques. In the SGcoSim frame w ork, it is possible to contact a serv er to solv e an optimization problem. The optimization problem should be written in a modeling language such as GAMS. then an xml le can be sent to a serv er such as NEOS SOL VERS [21]. It is also possible to inte grate optimization tools such as NLopt [22] and lpSolv e [23]. SGcoSim: a co-simulation fr ame work to e xplor e smart grid applications (Abdalkarim A wad) Evaluation Warning : The document was created with Spire.PDF for Python.
5110 ❒ ISSN: 2088-8708 Figure 1. Screenshot of OpenPDC manager 3. CASE STUDIES The follo wing subsections present tw o case studies considered in this study . The rst case study demonstrates ho w our tool can be utilized to e xplore real-time applications. The second case study illustrates the detailed use of an optimization frame w ork to enhance the operation of the po wer grid. 3.1. W ide ar ea monitoring W ide area monitoring consists of a set of measurement de vices that pro vide the control central with information in real-time to operate the grid reliably . This is i mportant during disturbances and dynamic conditions. This infor mation can be utilized to pro v ocati v ely perform the required steps to a v oid problems in the grid such as outages. PMUs collect and send it to PDCs. SGcoSim can be used to study approaches that require real-time data, e .g. po wer quality related approaches. F or these applications, t he simulator should run in real-time mode. 3.2. Integrated V olt/V AR optimization and demand r esponse (IVV O-DR) SGcoSim creates loads based on the bas eline load prole at the start of a simulation e xperiment. A central component can collect the essential data for a gi v en application using communication capabilities, and the controller can then transmit com mands to the v arious elements to operate the netw ork properly . V olt/V AR optimization can be utilized to optimize the v oltage prole using the a v ailable V AR resources. Lo w v oltage le v els can be maintained here, resulting in a reduction in po wer usage. Consequently , po wer losses will be decreased, as will o v erall ener gy usage. Simultaneously , we must mak e the most of the PVs. As seen in (1), the goal of the optimization problem is to reduce po wer demand and loss while maximizing PV utilization for T time steps and N b uses. min T X t =1 { Losses ( t ) δ t + N X i =1 ( P G ( t, i ) δ t − P S ( t, i ) δ t ) } (1) Where Losses ( t ) is the po wer losses at time t. P G ( t, i ) is the po wer generation on b us i at time t and P S ( t, i ) is the po wer from the solar panels. SGcoSim creates loads based on the baseline load prole at the start of a simulation e xperiment. A central component can collect the essential data for a specic application using the communication capabilities, and then the controller can process it. Se v eral limitations apply to this optimization issue. As seen in (2) and (3), the rst restriction is the po wer balance at each b us. Equations (2) and (3) represent acti v e and reacti v e po wer v alues respecti v ely . P G ( t, i ) + P S ( t, i ) + P D ( t, i ) − P C ( t, i ) − P L ( t, i ) − P E ( t, i ) Int J Elec & Comp Eng, V ol. 15, No. 6, December 2025: 5106-5118 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 5111 = N X k =1 v ( t, i ) v ( t, k )( G ik cos ( θ ( t, i, k )) + B ik sin ( θ ( t, i, k ))) (2) Q G ( t, i ) + Q S ( t, i ) − Q L ( t, i ) − Q E ( t, i ) + Q C ( t, i ) + Q B ( t, i ) = N X k =1 v ( t, i ) v ( t, k )( G ik sin ( θ ( t, i, k )) − B ik cos ( θ ( t, i, k ))) (3) The v alue of the reacti v e po wer generation at b us I is represented by QG ( t, i ) . The acti v e and reacti v e po wer from the solar system are represented by P S ( t, i ) and QS ( t, i ) . Acti v e and reacti v e load are represented by P L ( t, i ) and QL ( t, i ) . The battery po wer char ge and dischar ge are represented by P C ( t, i ) and P D ( t, i ) . The battery reacti v e po wer is QB ( t, i ) . Acti v e and reacti v e elastic loads are represented by P E ( t, i ) and QE ( t, i ) . The reacti v e po wer caused by a capacitor bank is QC ( t, i ) . The v oltage at b us I is v ( t, i ) . The real and imaginary components of the admittance from b us I to b us k are Gik and B ik , respecti v ely . The phase shift between b uses I and k is theta ( t, i, k ) . The loads at the dwellings are modeled as ZIP loads using the parameters described in [24]. The ZIP model depicts a load’ s change (with v oltage) as a composite of three forms of constant loads: Z, I, and P , which stand for constant impedance, constant current, and constant po wer , respecti v ely . The current acti v e and reacti v e loads are gi v en by the equations (4) and (5) as a function of the current v oltage (V). The design acti v e and reacti v e po wer are represented by the constants P 0 and Q 0 , respecti v ely . The design v oltage is v 0 . P L ( t, i ) = P 0 ( t, i ) " Z P v ( t, i ) v 0 2 + I P v ( t, i ) v 0 + P P # (4) Q L ( t, i ) = Q 0 ( t, i ) " Z Q v ( t, i ) v 0 2 + I Q v ( t, i ) v 0 + P Q # (5) Equation (6) represents the equation of solar panel acti v e and reacti v e po wer . P S ( t, i ) 2 + Q S ( t, i ) 2 ≤ ( S max S i ) 2 . (6) The in v erter’ s design imposes a restriction on reacti v e po wer . − S max S i sin ( ϕ ) ≤ Q S ( t, i ) ≤ S max S i sin ( ϕ ) . (7) The ener gy losses are sho wn in (8). Losses ( t ) = 1 2 N X i =1 N X k =1 G ik ( v ( t, i ) 2 + v ( t, k ) 2 − 2 v ( t, i ) v ( t, k )( cosθ ( t, i, k ))) The v alue of the v oltage at the costumer side must be within the standardized limits. The follo wing equation guarantees that the costumer v oltage v alue doesnt go be yond the acceptable limits. v min ≤ v ( t, i ) ≤ v max (8) The ener gy balance at the battery can be e xpressed as E ( t + 1 , i ) = E ( t, i ) + η P C ( t, i ) δ t − P D ( t, i ) δ t η , (9) where E ( t, i ) is the ener gy inside the battery at b us i at time t. The relation between the acti v e and reacti v e po wer with respect to the battery can be written as in (11) and (12). P C ( t, i ) 2 + Q bat ( t, i ) 2 ≤ ( S max bat i ) 2 (10) SGcoSim: a co-simulation fr ame work to e xplor e smart grid applications (Abdalkarim A wad) Evaluation Warning : The document was created with Spire.PDF for Python.
5112 ❒ ISSN: 2088-8708 P D ( t, i ) 2 + Q bat ( t, i ) 2 ≤ ( S max bat i ) 2 (11) The reacti v e po wer is limited by the po wer design f actor . − S max bat i ≤ Q bat ( t, i ) ≤ S max bat i (12) The elastic load should be run in a specic period (from T1 to T2). F or instance, an EV is considered as an elastic load E L i and should be ready at 7 AM. T 2 X i = T 1 P E ( t, i ) = E L i (13) The capacitor bank can be either on or of f. So we need a binary v ariable xc ( t, i ) to represent the relation between the installed capacitor bank C AP i and the injected reacti v e po wer Q C ( t, i ) . Q C ( t, i ) = xc ( t, i ) C AP i xc ( t, i ) ∈ { 0 , 1 } (14) This optimization is done in tw o stages. In the rst stage, we use a long optimization horizon (e.g. 24 hours) to nd the optimal periods to run the elastic loads. Then in the second stage, we run the optimization problem during the operation of the system with a short optimization horizon to nd the optimal set points of the dif ferent components such as reacti v e po wer from the PVs, batteries, and capacitor banks. The control v ariables are the v olta g e at the transformers, reacti v e po wer from the PVs, batteries, and capacitor banks, char ging and dischar ging time of the batteries, and the run time of the elastic loads. Each component measures and reports its po wer usage to the controller in order to apply this technique. The controller creates and transmits an optimization problem to a solv er . The results are returned by the solv er . After recei ving the results, the controller adjusts the v oltage at the load tap changer and sends the set points to the PVs, capacitor banks, elastic loads, and batteries. 4. EV ALU A TION In this section we e v aluate the tw o case studies. W e used a modied v ersi on of the netw ork presented in [25] as sho wn in Figure 2(a). 4.1. Data communication netw ork W e deplo yed 55 nodes in an area of size 800 m × 2,400 m. After the initialization of the cord, each node uses put to store its location on the cord. This w ay , an y tw o nodes can communicate in a peer -to- peer w ay . The nodes se n d the po wer and v oltage to the controller . The controller sends an XML le with the opti mization problem to a sol v er and when it gets back the solution, it sends commands to the dif ferent components. A partial vie w of the netw ork is sho wn in Figure 2(b). source bus B1 B2 B3 B4 B5 B6 B8 B7 B10 B11 B9 (a) (b) Figure 2. The test po wer grid and communication netw orks (a) the one-line diagram of the po wer grid and (b) partial vie w of the netw ork in OMNeT++. Each component such as a house or PV is equipped with a wireless node. Additional nodes are deplo yed to maintain the netw ork connecti vity Int J Elec & Comp Eng, V ol. 15, No. 6, December 2025: 5106-5118 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 5113 4.2. P o wer grid The n e tw ork consists of loads and PVs (solar panels), ener gy storage units, capacitor banks, and an on-load tap changer (OL TC). Standard loa d proles, which gi v e the acti v e po wer demand of f amilies as well as other types of loads, are used to produce demand and supply (e.g., companies and f actories). T o describe the stochastic beha vior of a single load, v alues are sampled from these proles and superimposed with stochastic functions. Some load proles and delay traces are sho wn in Figure 3. T ypical acti v e and reacti v e load prole are sho wn in Figures 3(a) and 3(b). The green l ine represents a residential load prole, while the red line represents a commercial load prole. W e assume that 2% of the load at each b us is elastic. W e dened the follo wing electricity netw ork conguration: a. Case 0 : 4 PVs, 20 kV A each, 2 storage systems 10 kw/13kWh each, a capacitor bank of size 10 kV A. b . Case 1 : This case is similar to case 0, b ut in this case we ha v e 11 PVs, i.e., a 20 kV A PV at each b us. c. Case 2 : This case is similar to case 1, b ut in this case we increased the PV to 40 kV A PV at each b us. d. Case 3 : This case is similar to case 1, b ut we increased the load by 25. At the be ginning, we look at the data communication netw ork and e xplore tw o important met rics, namely delay and number of hops. Figure 3(c) sho ws the cumulati v e distrib ution function (CDF) of the end- to-end delay in ms. About 80% of the pack ets need less than 10 ms to reach the destination. Figure 3(d) sho ws the CDF of the number of hops that pack ets tra v erse to reach the destination. Most pack ets (about 80% ) need less than 10 hops to reach the destination. During all simul ation e xperiments, data deli v ery rate w as 100% . (a) (b) (c) (d) Figure 3. Load proles and end-to-end delay (a) normalized acti v e po wer load prole for residential (green) and industrial (red), (b) normalized reacti v e po wer load prole for residential (green) and industrial (red), (c) CDFs of the end-to-end delay and (d) the path length Figure 1 sho ws a screenshot of OpenPDC, which recei v es data from a PMU. The data has been sent using the IEEE37.118 standard. OpenPDC can collect and store the data so that can be used for analysis. Critical data can be analyzed quickly to detect instabilities in the netw ork and react early to pre v ent serious problems. T o e xplore the inte gration of V olt/V AR and DR, v e dif ferent operating scenarios are dened as: a. Scenario 0 (Static conguration): The static conguration does not use the reacti v e po wer c apabilities of PVs and storage units and it holds the load tap changer at 415 v olts (line-to-line). b . Scenario 1 (V AR optimization): In this scenario we e xploited the reacti v e capabilities of the dif ferent elements such as PVs and storage systems. c. Scenario 2 (CVR optimization): In this scenario we changed the v oltage at the OL TC to reduce the po wer consumption in the electricity netw ork. d. Scenario 3 (IVV optimization): This scenario combines scenario 1 and 2. e. Scenario 4 (IVV O-DR): this scenario adds DR to scenario 3. Figure 4 compares the po wer at the transformer of the dif ferent scenarios with static conguration. The solid red line sho ws the po wer consumption of scenario 0 (static conguration) and the dashed green line sho ws the po wer consumption of scenarios 1 to 4. As can be seen in Figure 4(a), the dif ference between static conguration and V AR opti mization is minimal. In particular at lo w demand periods, CVR Optimizat ion has more po wer sa vings compared to V AR optimization, as can be seen in Figure 4(b). Inte grating CVR and V AR approaches t ogether (IVV optimization) mak es it possible to reduce the po wer also at higher demand periods as can be seen in Figure 4(c). F or the IVV O-DR scenario, the controller has mo v ed some load from the high demand period to the lo wer demand period when including DR and the sa vings are e v en more clear as can be seen in Figure 4(d). SGcoSim: a co-simulation fr ame work to e xplor e smart grid applications (Abdalkarim A wad) Evaluation Warning : The document was created with Spire.PDF for Python.
5114 ❒ ISSN: 2088-8708 (a) (b) (c) (d) Figure 4. Po wer at transformer with 4 PVs, 20 kV A each, for the scenario static conguration (solid red lines) and the four optimization scenarios (dashed green lines) V AR optimization (a) V AR optimization, (b) CVR optimization, (c) IVV optimization, and (d) IVV O-DR T able 1 summariz es the ener gy consumption and losses of the dif ferent scenarios during 24 hours. V AR Optimization has the lo west ener gy losses, b ut the reduction of demand is not high compared to CVR Optimization. CVR has the highest ener gy losses and e v en higher than the static scenario. This is due to the f act that lo wer v oltage leads to higher po wer loss es. Inte grating CVR and V AR (IVV Optimization) leads to better results re g arding both; demand and losses. No w inte grating DR (IVV O-DR) leads to e v en more sa vings and a lo wer peak demand. W e also e xplored the v oltage at the b uses. As can be seen in Figure 5, applying V AR optimization impro v es a little bit the v oltage prole, i.e., it increases the v oltage at the end-user side due to the V AR inject ion in particular when the load is high as can be seen in Figure 5(a). CVR optimization tries to k eep the v oltage as lo w as possibl e to reduce the po wer consumption based on the ZIP load model as can be seen in all other scenarios in Figures 5(b), 5(c), and 5(d). T able 1. Results: demand and losses of the dif ferent scenarios Approach Demand (kWh) Losses (kWh) Scenario 0 (Static conguration) 6985.8 364.2 Scenario 1 (V AR) 6974.6 339.2 Scenario 2 (CVR) 6905.7 375.4 Scenario 3 (IVV O) 6861.8 352.6 Scenario 4 (IVV O-DR) 6849.8 343.2 (a) (b) (c) (d) Figure 5. V oltage at b us 5 with 4 PVs, 20 kV A each, for the scenario static c onguration (solid red lines) and the four optimization scenarios (dashed green lines) (a) V AR optimization, (b) CVR optimization, (c) IVV optimization, and (d) IVV O-DR Figures 6 compares the po wer at the transformer of IVV O-DR with the static scenario for cases 1, 2, and 3 when increasing the capacity of the solar system. F or all cases, IVV O-DR has a lo wer po wer consumption in particular at the e v enining as can be seen in Figures 6(a), 6(b), and 6(c). T able 2 summarizes the ener gy demand and losses for these cases during 24 hours. Figure 7 sho ws the v oltage for the dif ferent cases. Increasing the PV increases the v oltage as can be seen in Figure 7(a). Static Conguration can lead to o v er -v oltage when the generation is high and the load is lo w as can be seen in the middle of the day in Figure 7(b). This happens because of the re v erse po wer o w that res ults from the high generation of the PVs. T o enable the re v erse po wer o w , the v oltage at the PV side should be higher than at the transformer . This means, the v oltage should be higher than 415 v olt. No w reducing the v oltage at the transformer can alle viate the o v er -v oltage problem, ne v ertheless it leads to another problem at the high demand periods. Therefore, static congurati on is not suitable for the current/future po wer grid. W e increased the demand by 25%. The v oltage at b us 5 is sho wn in Figure 7(c). Here we see the under -v oltage problem during tw o periods. Int J Elec & Comp Eng, V ol. 15, No. 6, December 2025: 5106-5118 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 5115 (a) (b) (c) Figure 6. Po wer at transformer for the scenario static conguration (solid red lines) and scenario IVV O-DR (dashed green lines) and the three cases (a) 11 PVs with 20 kV A each, (b) 11 PVs with 40 kV A each, and (c) 11 PVs with 20 kV A each and additional 25% load T able 2. Results: demand and losses of the dif ferent cases Approach Demand (kWh) Losses (kWh) Case 1 (Static Conguration) 6273.1 305.8 Case 1 (IVV O-DR) 6095.8 281.8 Case 2 (Static Conguration) 5241.6 296.3 Case 2 (IVV O-DR) 5015.4 272.1 Case 3 (Static Conguration) 8172.6 516.6 Case 3 (IVV O-DR) 8028.5 461.7 (a) (b) (c) Figure 7. V oltage at b us 5 for the scenario static conguration (solid red lines) and scenario IVV O-DR (dashed green lines) and the three cases (a) 11 PVs with 20 kV A each, (b) 11 PVs with 40 kV A each, and (c) 11 PVs with 20 kV A each and additional 25% load 5. CONCLUSION In this paper , we introduced SGcoSim, a co-simulation frame w ork designed to e xplore smart grid applications. W e proposed the use of WSN approaches to establish a eld area netw ork, enabling the inte gration of v arious smart grid applicat ions. The VCP w as emplo yed to f acilitate ef cient peer -to-peer communication among grid components. W e demonstrated some capabilities of SGcoSim through tw o distinct smart grid applications. The rst applicati on in v olv ed wide-area monitoring, which relies hea vily on real-time communication. In the second application, we IVV O with DR, termed IVV O-DR, to ef fecti v ely reduce ener gy consumption and po wer losses in an electricity distrib ution netw ork. Our results indicate that combining V olt/V AR optimization with demand response signicantly decreases both the o v erall po wer demand and system losses. Although demonstrated with thes e e xamples, SGcoSim is v ersatile and can be adapted to study v arious other smart grid applications and challenges, including c ybersecurity threats and additional operational issues. As future w ork, we will w ork on e xtending the SGcoSim frame w ork to in v estig ate c ybersecurity issues such as f alse data injection, denial-of-service, and spoong attacks. A CKNO WLEDGEMENT The authors w ould lik e to ackno wledge the German Federal Ministry for Education and Research (BMBF) and the P ales tinian Ministry of Education and Higher Educat ion (MOEHE) for the nanc ial support under P ALGER2015-34-046 (CSFPPG). FUNDING INFORMA TION This w ork is partially funded by German Federal Ministry for Education and Research (BMBF) and the P alestinian Ministry of Education and Higher Education (MOEHE) for the nancial support under SGcoSim: a co-simulation fr ame work to e xplor e smart grid applications (Abdalkarim A wad) Evaluation Warning : The document was created with Spire.PDF for Python.