Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks

Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks

Citation Author(s):
vahid
kouhdaragh
daniele
tarchi
alessandro
vanelli coralli
Submitted by:
Vahid Kouh Daragh
Last updated:
Wed, 10/16/2019 - 19:47
DOI:
10.21227/smc2-2s42
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Abstract: 

Smart Grids (SG) are a novel paradigm introduced for optimizing the management of the power generation, transmission, distribution and consumption. A SG system can efficiently work only if all the components are connected through a communication network able to satisfy the SG applications requirements. Wireless communications are the most appropriate candidates for handling SG requirements due to their flexibility. By gaining from the presence of several Radio Access Technologies (RATs) and different SG applications, a Cost Function (CF) approach is here proposed based on both communication and energy consumption aspects for optimizing the SG nodes assignment to the different RATs. The proposed method allows to optimize the SG node allocation by exploiting the variable spatial densities of the nodes. The numerical results are achieved by using MATLAB simulations.

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Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks
Vahid Kouhdaragh, Daniele Tarchi, Senior Member, IEEE, and Alessandro Vanelli Coralli, Senior Member, IEEE
Abstract—Smart Grids (SG) are a novel paradigm introduced for optimizing the management of the power generation, transmission, distribution and consumption. A SG system can efficiently work only if all the components are connected through a communication network able to satisfy the SG applications requirements. Wireless communications are the most appropriate candidates for handling SG requirements due to their flexibility. By gaining from the presence of several Radio Access Technologies (RATs) and different SG applications, a Cost Function (CF) approach is here proposed based on both communication and energy consumption aspects for optimizing the SG nodes assignment to the different RATs. The proposed method allows to optimize the SG node allocation by exploiting the variable spatial densities of the nodes. The numerical results are achieved by using MATLAB simulations.

Index Terms—Smart Grids, Radio Access Technologies, Cost Function Optimization, Resource allocation, Heuristic Optimization
I. INTRODUCTION
The conventional power grids are becoming less attractive mainly due to their reduced energy efficiency. Smart Grids (SG) introduce a novel power system paradigm aiming at managing power generation, transmission, distribution and consumption in an efficient way. In order to work properly, SGs require a tightly coupled communication infrastructure allowing a bidirectional information flow between the power grid devices (i.e., the SG nodes) and an operational center. Bidirectional communications allow lowering energy consumption and outages thanks to a smarter power distribution management. SGs are introduced to accomplish these goals [1],[2].
SG systems are composed by different types of nodes, having different communication requirements and functions. The nodes types should exchange command, controlling and demand response messages among them and with a centralized operational center. For pursuing the message exchange, it is possible to gain from several different communication technologies. Among other, wireless technologies allow to deploy the system in a much more efficient way by exploiting the wireless communication flexibility [1]. However, there are several potential Radio Access Technologies (RATs) with different communication configurations and characteristics able to cope with the SG communication requirements [2]. On the other hand, radio spectrum is becoming a scarce resource due to its exponentially increasing demand [3][4], thus, resource allocation for supporting different SG node types should be optimized while respecting the SG node types communication requirements.
The node allocation in heterogeneous networks is a long-standing problem considered in the literature. In [5] the authors consider a round robin approach for load balancing where the communication traffic is evenly distributed among all the available base stations, regardless of existing load and performance issues. However, the proposed approach does not consider RATs characteristics, SG node communication requirements and densities, resulting in designing an inefficient heterogeneous network. The distance is the only parameter used to balance the network traffic. Another load balancing method is called Predictive Nodes Method in which all the available base stations are observed over time and the trends are analyzed [6]. The load balance is performed in a way that the traffic is assigned to the base stations that have the best performance in terms of delay, data rate and energy efficiency. Moreover, this approach requires a cognitive process during the observing period for sensing and finding the results with a higher delay [7]. In [8] the suitability of different RATs deployment for different SG applications is assessed by considering the enterprise level, generation, transmission and distribution levels, to the end customer level. Data rates and coverage ranges of wireless communication technologies were compared. An assessment is performed to evaluate the different communication technologies for enabling different smart grid applications based on specific network requirements. Usually, optical fibers, xDSL, coaxial cable, and powerline communications are considered as wired solutions and ZigBee, wireless mesh, WLAN, Z-Wave, WiMAX, cellular, and satellite as wireless solutions.
Our idea is instead based on defining a suitable Cost Function (CF) able to model the different system requirements, so that its minimization allows to efficiently allocate the node types to the different RATs. Differently from other approaches where only a partial view of the system has been considered, we are here proposing a suitable mapping method allowing to quantitatively measure different qualitative requirements and map them over a scalar function that can be easily managed. Moreover, the proposed CF is composed by two parts, namely the Communication CF (CCF) and the Energy CF (ECF) in order to consider both communication and energy issues when optimizing the nodes allocation.
The CCF is defined through the Key Performance Indicators (KPIs) of the SG node communication requirements and RAT communication characteristics. Four different KPIs have been considered, i.e., data rate, delay, complexity and security, modeled by using several parameters, including modulation order and spectral efficiency, network latency, encryption method, packet loss probability. A suitable value-based mapping of qualitative-based functions is introduced. This approach allows to achieve a quantitative measurement of the suitability of the considered RATs when operating in a heterogenous SG nodes scenario. The SG node density has been also considered as an input when measuring the different parameters. The CCF allows to maximize the appropriateness between the RATs features and the nodes communication requirements [2][9]. At the same time, a proper ECF is defined for giving importance to the energy consumption issues of the different RATs during the communications [10],[11].
In this paper different SG node types with different communication requirements are considered. Moreover, the effect of the SG node types densities in radio resource allocation, is discussed. Five different RATs (i.e., GSM, LTE – for the terrestrial links – and Low Earth Orbit (LEO), Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) – for the satellite links) are considered. The results show the effects of the CF based allocation with different densities of the SG node types. The assessments performed in this study are useful for selecting appropriate RATs for different SG node types scenarios. In this work, 16 different SG node types and the related communication requirements are introduced. Moreover, three different scenarios with different areas and node densities are considered. The comparison among them is performed when optimizing the nodes allocation among the different RATs.
The following of the paper is structured in this way. In Section II the SG node types and their communication requirements and data collecting methods are introduced. In Section III the CCF, the ECF are introduced, as well as the proposed node assigning method. In Sections IV and V, the numerical results and conclusions are given, respectively.
II. SMART GRID COMMUNICATION SCENARIO
A generic data collecting model, as depicted in Figure 1, is supposed to be deployed in a SG area, where the data are collected from the nodes by an aggregation point through a dedicated communication system (e.g., IEEE 802.15.4, LoRaWAN, Wireless M-Bus), while we suppose to collect the data from the aggregation point to the Control Station (CS) through a RAT [2][12]. We focus our attention on dimensioning this second link. In particular, by supposing that each aggregator can select among different RATs, it is possible to adopt a density-based RAT selection through the use of intermediate aggregation points. The RATs we have considered are LTE, GSM, and links exploiting LEO, MEO, and GEO satellites. Each RAT has been modeled through several communication parameters, i.e., Spectral Efficiency (SE), Modulation order, Forward Error Correction (FEC) scheme, Packet Loss Probability (PLP), Round Trip Time (RTT), and Processing time. The considered values are introduced later in the Section IV where the numerical results are obtained.
The communication model is supposed to be structured in a way that each aggregation point collects the packets from the SG nodes belonging to its area and relays them to one of the possible RATs deployed in the area. On the other side, due to the high number of nodes, we suppose that each aggregator can queue the packets in a transmission buffer in case of increased requests.
In the scenario we are assuming the presence of different node types. The first node type that we are considering is the Wide Area Situational Awareness (WASA) [4][12][13]. Since WASA is used for different purposes, with different requirements, we have classified the WASA into 9 possible subclasses. Adaptive islanding, predictive under frequency load shedding, wide area power oscillation damping control are considered as node type WASA1. Wide area voltage stability control and cascading failure control can act as WASA2. Flexible AC Transmission System (FACTS) and High Voltage DC transmission (HVDC) control and pre-calculation transient stability control can act as WASA3. WASA4 considers closed loop transient stability control. WASA5 considers Dynamic state estimation nodes. WASA6 includes Phasor Measurement Unit (PMU) assisted state estimation [4]. WASA7 considers wide area voltage stability monitoring. WASA8 represents local voltage stability monitoring. WASA9 stands for local power oscillation monitoring.
In a similar way Distributed Grid Management (DGM) node types includes 4 different subclasses. DGM1 includes Fault Location, Isolation and Restoration (FLIR) nodes. DGM2 includes the Asset Management nodes. DGM3 includes the Optimization nodes. DGM4 includes the Workforce Access nodes.
Finally, Distributed Energy Resource (DERS) takes into account the Distribution customer storage node (charge/discharge command from DAC to the storage), PHEV (Plug-in Hybrid Electrical Vehicle) nodes are related to Electric transportation, and Smart Meters (SMs), which collect the local data and a set of SMs within an AMI infrastructure, are considered [4][12][13].
Table 1 shows the SG node type requirements and characteristics for the uplink based on [4][9][14][15]. In Table 1, packet size corresponds to the reference packet length generated in each time slot from each node, the delay corresponds to the delay requirement within which data should be received by the CS and inter-time packet generation interval. Finally, a security qualitative value, showing the security requirement by each node, and a reliability value showing the reliability requirement in percentage for each node are shown.
16. SMs 1000 2 900 High 99-99.99%
Communication And Energy Cost Functions
In order to efficiently allocate the SG nodes to the different RATs a suitable CF is here defined. Since each SG node type has, on one side, different requirements and the RATs, on the other side, have different characteristics a suitable mapping approach should be used. Moreover, the number of nodes of each SG node type impacts in a different way the SG allocations and the respect of the different requirements. Goal of the proposed approach is that of defining the optimal fraction of each SG node type to be allocated to each RAT in order to maximize the system efficiency. Such allocation can be performed through the presence of aggregators able to select the best RAT among those present in the area. Due to the large numbers of parameters affecting the RATs a suitable mapping approach is here proposed. The quantitative cost, that matches the SG nodes’ requirements and the RATs characteristics, can be achieved through the definition of proper CFs. Such CFs are introduced for designing an efficient HetNet able to respect the different SG node types requirements.
While in [9] a CF based approach where both communication and energy issues are considered for a singular scenario, in this paper we design the CF for different node densities and different node numbers. The goal is to show how efficient allocation can be implemented when the numbers of nodes are changed. As it can be seen, for different scenarios in terms of node densities, different percentage of allocation is achieved for the same type of nodes.
In order to cope with both communication and energy requirements a joint Communication CF (CCF) and Energy CF (ECF) is here defined. The CCF is designed aiming at identifying the priority of each available RAT by evaluating its effectiveness in supporting the SG node types through four KPIs, i.e., data rate, delay, reliability and security. The CCF is then used jointly with the ECF, in order to define the percentage of node types to be assigned to each RAT. The approach used for defining the ECF is based on a tradeoff between the communication performance and the energy consumption. Hence, the proposed approach allows to jointly consider both communications and energy aiming at maximizing them jointly.
If N_KPI is the number of communication KPIs, W_i^q and N_ij^q are the weights of the q-th KPI for node type i and the normalized value of the q-th KPI for RAT type j when used by the node type i [5], respectively, the communication CF can be written as:
C〖CF〗_ij=(∑_(q=1)^(N_KPI)▒〖W_i^q⋅N_ij^q 〗)/(∑_(q=1)^(N_KPI)▒W_i^q ) 
with i∈{1,…,N} and j∈{1,…,F}, where N is the total number of node types and F is the number of available RATs. The KPIs considered in this work are data rate, delay, reliability and security, hence, (1) is rewritten as [5][16]:
〖CCF〗_ij=(W_(R_i )⋅N_(R_ij )+W_(D_i )⋅N_(D_ij )+W_(RE_i )⋅N_(RE_ij )+〖W_SE〗_i⋅N_(SE_ij ))/(W_(R_i )+W_(D_i )+W_(RE_i )+〖W_SE〗_i ) 
where 〖CCF〗_ij is the communication CF for the user type i when using the RAT j, W_(R_i ) and N_(R_ij ) are the data rate weight and normalized value for user type i and RAT type j, respectively, W_(D_ij ) and N_(D_ij ) are the delay weight and normalized value for user type i and RAT type j, respectively, W_(RE_ij ) and 〖W_SE〗_ij are weights for reliability and security respectively, and N_(RE_ij ) and N_(SE_ij ) are the normalized values for reliability and security, respectively.
In Fig. 2 the CF working process is depicted. Different SG node types are considered, and the related communication requirements. Then area size and density of each node is determined. The characteristics of RATs are defined as well. Following this, these parameters are fed into the CFs block and, based on the proposed method, a certain percentage of traffic of a certain node type is assigned to be supported by each RAT. In order to be used we must define the weight of each KPI and the KPIs normalized values.

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[1] vahid kouhdaragh, daniele tarchi, alessandro vanelli coralli, "Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/smc2-2s42. Accessed: Dec. 12, 2019.
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title = {Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks},
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T1 - Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks
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vahid kouhdaragh, daniele tarchi, alessandro vanelli coralli. (2019). Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks. IEEE Dataport. http://dx.doi.org/10.21227/smc2-2s42
vahid kouhdaragh, daniele tarchi, alessandro vanelli coralli, 2019. Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks. Available at: http://dx.doi.org/10.21227/smc2-2s42.
vahid kouhdaragh, daniele tarchi, alessandro vanelli coralli. (2019). "Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks." Web.
1. vahid kouhdaragh, daniele tarchi, alessandro vanelli coralli. Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/smc2-2s42
vahid kouhdaragh, daniele tarchi, alessandro vanelli coralli. "Joint Communication and Energy-Based Density Aware Smart Grid Node Resource Allocation in Heterogeneous Networks." doi: 10.21227/smc2-2s42