Experimental and correlational designs

The introduction of smart grids has the important role in the transformation of functionalities in the latest grid s of energy for the aim of providing user-oriented services as well as an assured high security, economic efficiency and quality of the supplied electricity in the environment of markets [1]. In addition, the expectations of smart grids are its key enabling feature in the change to low-carbon sectors in energy thereby ensuring sustainable and efficient consumption of natural resources.  Energy production from sources that are renewable that include; photovoltaic and wind units, however, are naturally intermittent. It, therefore, would mean a lack of correlation in the energy local consumption and production. Furthermore, the need for dispatchable, flexible is increasing, generation of fast-ramping energy that balances variation in loads and contingencies that include the transmission losses or the generation of assets [2]. There is a development of these similar problems at the market level since the local and national balance between demand and supply get complicated when managing greater levels of renewable energy [1].

Smart structures can be grouped into three major cores, first is the smart protection system that relates to the system’s reliability, security and failure protection. The targeted optimization areas include the grid-self-healing, information monitoring and data recovery as well as measurement. Secondly, systems having smart management deal with the utility, cost, energy efficiency and demand profile. Lastly, systems with smart infrastructure are grouped into sub-classes of smart energy, communication system and information [4].

Sources of renewable energy can be integrated with two-way communication existing between the consumer and the grid which is the focused infrastructure optimization part in the system. Effective demand and supply side need multiple protocols of communication, communications-enabled with wide area network between the substations and electric utility. Neighbour Area Network, Field Area Network, as well as Advanced Metering Infrastructure which jointly facilitates communication between consumers and substations. An expensive technology such as the Independent Power Communication Line is used in optimizing cost efficiency through the combination of simulators in power networks and simulators in communication networks. Practically, power synchronisation and communication networks are one major problem. Furthermore, advanced infrastructure used in communication mostly make use of wireless protocols for reasons such as effective work. However, such networks possibly have reduced protection with their combined communication and power. This could be the leading cause of major damages if a malfunctioning occurs in the system [5].

Ethical issues in research involving human participants

Penetration in a distributed generation into systems of power distribution support the demand of energy is effective in dealing solving energy crisis. Also, the systems of energy management are efficient, however, the DG integration is challenging due to problems in the smooth flow of power. Battery storage and renewable energy systems are integrated with the grid power distribution resulting in unbalancing of power in the reactive power and active power [6]. The network’s power instability is considerably noticed due to the fluctuations of voltage. This problem can be corrected with the use of two-way communication method that distributes generation. Systems with two-way communication have intelligent control strategy that avoids conflict between user devices and provides smooth regulation of voltages [6].

The quality of power in the concerning factor in the distribution of power and installation of units produced from renewable energy in the system’s distribution is affected on the characteristics and functionality in the regulation of voltages. Swags, voltage dips and swell events featured and could lead to poor quality of power. Traditionally, Step Voltage Regulator, control structures and Feeder Shunt Capacitors are applied in systems with conventional power distribution. Smart proposals for advanced control structures in the shape of FSC and SVR agents could reduce the problem of voltage regulation. Such applicable strategy would surpass the problem due to unbalancing of reactive and active power [7].

Voltage deficiencies identification at the side of energy supply could be used in their proactive technique and the same method could reduce the complaint numbers at the demand side. Such a solution is able to decrease the complaint numbers would fail to minimize the occurrence of these events, therefore, the self-healing approach is able to be applied at this juncture to solve the problem. Forecasting and predictive technologies in smart power grids make use of algorithms that are able to provide effectively protected systems [8].

Smart grids in the Demand Side Management have to be explored and a variety of load shifting methodologies could be implemented for effective provision of energy management. Demand-side smart loads are able to be integrated with the exchange data and cellular networks while making use of the advanced infrastructure in metering [9].

Generally, there are two major problems that relate to communication and power networks highlighted as two-way communication and voltage regulation structure for overall effective renewable sources of energy distribution. Voltage regulations could be accomplished by substation shunt and load tap charging agents. Synchronization and latency issues in advance communication systems could be monitored by AMI for increase communication efficiency and response back during troubleshooting [10].

Introduction to smart grids and their expectations

Taking note of the concept in the DSM, the distributed energy storage and distributed energy generation could be recognizable in that they are major facilitators in the deployment of smart grids. This is due to the problems that come with the integrated renewable sources of energy which are minimized during dispatch and the DS, distributed energy sources, as well as the DG, distributed energy generation, being incorporated into innovative DSM methods and the electricity network demand side in a simultaneous implementation [12]. A combination of DS, DG and DSM techniques would lead to a system that has diverse generation sources that supply energy across the smart grid to bigger sets of uses on the demand side [13]. The energy efficiency would possibly improve as well as the local generation and load control. The DSM, therefore, could be referred to as the variety of initiative time modification that is intended to develop the demand magnitude as well as the patterns [13]. Advance mechanisms can be introduced for the purpose of encouraging the active participation of the demand-side in the process of active optimization. Hence, users of the demand –side get supplied with control devices, widely known as smart meters. These meters are able to manage the demand of energy and facilitate the communication with the supply-side [14].

The response that is faster is provided by the variety of (DR) Demand Responses. Specifically, a broadcasting signal such as the transmission or distribution system operator (TSO/DSO). The signal may be able to contain a command or a price for the shifting/shedding. It is not important for the deadline to be instantaneous. The signal could refer to an instance at 12 noon as the there is a possibility of the grid emergency being anticipated [15]. There is an assumption by the Classical Direct Load Control to laud fully when under control in they do as they are told [15]. Every intelligence is supposed to be in the controller that ideally makes use of the load model in the production of reasonable decisions. A use of a stochastic model that are state-space in loads together with the simulation of urban power systems. A technology of this nature has shown both transport and cost losses. Open ADR is an example of a modern system in the demand response automation. It is developed with the use of a leading DR research group known as the Demand Response Research Centre. OpenADR has an open-source and open specification reference for a distributed implementation. The DR orientation of the client-server infrastructure is published I the model of the subscriber. The main components include [16];

• DRAS that has the site of the consumer clients.
• Demand Response Automation server.
• The communication infrastructure is the internet.

Cores of Smart Structures and their focus

The side of the client is usually a library for communication that is made use of by control manufacturers in making the product capable of OpenADR. Programs are available for client DR subscription such as the demand bidding and the critical peak pricing with the DRAS being used as the financial incentives that are connected for reaction to these events [17].

The system is nearly an open-loop in that neither the online feedback nor the load models being used. Ripple control is combined using the wide area system in phasor measurement that uses GPS timestamps. The timestamps are distributed using current/voltage measurement equipment [18]. An addition of results from the loop feedback is wide in the system energy control a 10% assumption in the load control 1Broadline powerline communicates a nighttime in the storage of electric stoves [19]. An analysis of the DSM business impacts produces models that are used in analyzing business models having different simulated electric devices. These are the shiftable loads, loads having storage as well as the real electric storages [19].

This paper proposes which DSM method, B2G or C2G, that could be used in a smart grid power system for the purpose of optimization with the attempt to facilitate a reduction of the monetary expense time period during analysis by production and storage of energy rather than the purchase of their needs of energy from the smart grid. Taking into consideration the user selfish nature, a theoretical approach would particularly suit a calculated optimum storage and production strategies. For the above-mentioned reason, the modelling of the B2G model is recommended [20].

A recent source, [21],led to the development of real-time pricing schemes with the aim of reducing the ratio in the peak-to-peak average lauding through the management of the response demand in systems of the smart grid system. A 2-stage problem in optimization is proposed and later solved. The author comes up with a developed stochastic model that schedule local area networks with the aims being a minimization of cost as well as PAR minimization.

Studies in [22] present formulation in linear programming that are intended to minimize the cost of energy through direct control of the energy. A robust approach to the optimization presents an adjustment hourly level of the load for a specific consumer responding to the hourly price of the electricity. These renewable energies uncertainties, however, were not added to this source. Hence, schemes used in controls were not readily optimized and be applied to the smart grid scenario that constituted of renewable energies for significant portions of resources of power.

Integration of Renewable Energy and Two-way Communication

Considering an improvement in energy efficiency, [23] says that a start would be obtaining insight and information for the process to be involved. Every consumer site would practically have hidden problems that lead to wastage of energy; these include the compressed leakages of air, dirty filters, misconfigured controls and broken equipment. This trivial problem could be overlooked unless there exists a tool that would be used in analyzing the efficiency of energy as shown in this source. The parts of information energy that exist include [24];

• Infrastructure for data acquisition. These are the data loggers, sensor networks, modems and gateways.
• A server application possessing a database, analysis algorithms, calculation, reporting and alarming.
• The interface of the users that configures and visualizes.

Classical calculations that exist include [25];

• Comparison of the baseline to the peak loads. High baselines might stem due to an old equipment or a standby power.
• Weekly time series comparison. Often ventilation and lighting accidents run through the weekend and the night [1].
• Benchmarks. The performances are compared to others, especially this is critical in multisite users like the supermarket chains.
• Process correlations. The consumption of energy could be checked to strongly correlate with the temperatures on the outside or the gains in solar.

Other than these static figures inefficiencies, an added dynamics are evident in such systems. Experienced managers of these facilities or use of smart algorithms make it possible for consumption pattern interpretation and find ways for reducing the peak loads [27]. If the supply of energy leads to contracted penalized peaks, making it a valuable result. An occurrence of changes in logistics may not help, only equipment automation is suggested to be a requirement [27].

If an equipment operation requires driven consumption adjustment, there could be an implantation of an energy controller. These types of devices are mainly located in the energy metre to make it possible for the trend of energy consumption to be monitored as stated in [28]. Additionally, the monitored trend indicates unwanted levels, the equipment is switched off by the controller based on the set priorities as well as other rules. The orientation was depicted as shown below;

Another source comes in to mention the configuration of the energy controller to be a task that is very complex. To add on this, the complexity would increase once the consumers begin to be added and removed. [29] goes on to state that the chosen rules would determine the stability. An example simple enough for determining the priority level was a dependence on the trend in consumption. A better presentation was set as shown in the graphs below;

In these graphs, the author depicts the measurement period that has a country-dependent line time of about 15 or 30 minutes that represent the smallest period of time for billing reasons. Every period, zero is the starting point and later moves monotonously upwards. The bottom part of the curve shows where most of the power is consumed that is steeper in the curve of energy consumption. Once the trajectory of consumption of power crosses either of the upper lines of the threshold, some classes or groups of consumers get switched off. The above graphs show three devices that are classed. Ci represents the most important devices, c3 is the least important classes [30]. The trajectory begins when it is steep as every device is turned on initially. The graph exhibits steeper nature than the ideal curve that is dashed hence it goes to cross c3 off turning it off that is the least important for first measures to make the curve flatten. Next is c2 that gets crossed and turned off thereby making the devices categorized as c1 to be the ones on. The resultant flat line curve would then cross the c1 line but doesn’t turn it off as these devices are left on anyway. When the lines cross c2 again the devices get turned on and so would the c3 devices ones their line gets crossed. The development of such a system would make it possible for the consumption energy goal to be attained [31].

Managing Distributed Generation and Voltage Regulation

Distributed Spinning Reserve

[32] proposes a distributed spinning reserve that is aimed at supporting traditional ancillary service providers in imitation of their behaviour. The investigation as seen on the demand side meant that these loads could be increased or reduced at instances that the grid frequencies rose or dropped. There are two implemented schemes which are the Integral Resource Optimization Network that is discussed in [33] and the grid-friendly controller discussed in [33]. These schemes help in measuring frequency and reacting to it. However, the IRON differs from the GFC due to the added communication interface allowing cooperate algorithms.

[35] adds on the subject to discuss the add-on feature of the communication fairness capabilities. Taking an example of various devices shedding their load and one of these devices having insufficient shedding. The existing communication makes it possible for arranging the devices to all have their turn. Developing this coordination allows stability in the network. A community that could have this autonomy and distributed controllers facilitating communication would react to the problems of the grid similar to the manner that a perfect unstable recipe. The users would be forced to do it in an orderly manner for avoiding a strong reaction. Such a mechanism distributes control in a classical manner. It requires the frequency to drop to allow the controllers to react. Also, it requires restoring to prevent regular fixing.

[14]produces a version that is more sophisticated going into the controls that is more-predictive. Devices that service load models of themselves might be able to predict how long or much the load could be shed until the shedding is topped for process reasons. The models of the load are the steps followed towards stability. They provide answers to questions of the reaction strength to an anticipated problem needed as well as the person that can provide it.

Demand Shifting

The load models could be used in the demand needs for shifting to other periods. In case other factors such as weather are predicted, an emergency in the grid is set in the next day at around 1730. Therefore, [12]produces a research that makes it possible for intelligent users to plan ahead. This could be done only if the process accepts the planning that may be later or earlier. Some of the examples include; stock production or precooling. Shifting is then to be done on processes that typically are categorized in one of the lists below;

• Processes in inert diffusion such as irrigation and ventilation.
• Processes that are thermally inert such as cooling and heating,
• Logistics such as dependencies, schedules and lunch-breaks.
• Mass transport such as conveyor belts and pumps having tanks.

Benefits of DSM

Load shifting to later points in time is easily done. In instances of critical time, the load has to be shed with the same process being made to catch up at later periods. However, the quality of the process that is suggested by ################# is not that much guaranteed. If there misses being proper stocking of products or in instances of the tank being almost empty, the process may get into trouble when it attempts to shed. The author, therefore, recommends the peak be moved before the time for shedding and preparation to be important. In this model, loads are required. They avail an anticipated duration of things to be switched off, the duration required for filling the virtual storage as well as the cost. The demand shifting virtual storage can be enhanced by special means. The researcher goes on to show an instance of phase change in a material building with an electric heater to increase the low thermal structure of inertia.

Virtual Storage loads Power plants

In a community, Virtual Power Plants are typical small generation units that are depicted as one source having renewable sources of energy appearing as one grid manage power plant as discussed in [35]. A distributed equipment typically requires a controlled from centralized management and dispatch node with modern SCADA standards used for integration for the individual parts. Special cases arise in such load parts. These loads do not generate, they only are virtual storages through load shifting. An aggregate of numerous of these types of loads leads to the development of sizes capable of participating on the power markets as well as competing with traditional storage of electricity. Aggregators typically use proprietary technologies in doing this, however, IEC makes a better candidate in enabling the interoperability.

[35] mentions that the most important point of VPP is the crucial time in load-based virtual power plants storage, a guaranteed availability. If the operators of the grids request certain amounts of power regulation, the researcher states that this could be delivered. However, numerous loads behave in a stochastic manner. A process for customers might in that instance not be interruptible or the virtual storage could be empty. In this model, once more, reliable load models would be advisable for necessary making the VPP operator possess promises that are available [37].

Load Management Communication Protocol

[37]Researched standard series of IEC used in the automation of substation. The source contains a scheme for addressing IEDs or the DERs as well as their functions and properties. A substation based on the XML is configured in languages, protocols of communication for preferred efforts together with real-time transport and so much more.  The source states that modern manufacturer’s implement the technology in the latest products produced in power engineerings such as distributed automation nodes or the diagnostics of devices and the grid measurements.  The model is best implemented in VPPs and gradually makes way down to the distributed level [40].

OpenADR: A Modern Demand Response Automation System

There have been several groups that publish the energy service standards in the home. One of the groups recommended by [38] is the Zigbee Smart Energy Profile. The research provides an extension of the description of the document for the large numbers of meter reading services, security information, online pricing as well as load control. A less descriptive but also interesting proposal would come from the American Society of Heating, Refrigerating and Air-Conditioning Engineers. There is a defined machine state abstract representing loads without or with load shedding properties.

Research Design

a)Research Question

This paper proposes which DSM method, B2G or C2G that could be used in a smart grid power system for the purpose of optimization with the attempt to facilitate a reduction of the monetary expense time period during analysis by production and storage of energy rather than the purchase of their needs of energy from the smart grid [22]. Taking into consideration the user selfish nature, a theoretical approach would particularly suit a calculated optimum storage and production strategies. For the above-mentioned reason, the modelling of the B2G model is recommended.

b)Related work

In Consumer-to-Grid (C2G) model systems, the energy being consumed is set to be visualized with the use of a system that communicates back and forth relaying the information of the consumer [40]. The method tries to engage the consumers present in the household sectors [24]. This method was chosen due to its ability to reduce the total consumption of electricity averagely to about 7% in one year. Consumers have been using this technology for some time but its popularity has reduced over time. In this mart grid methodology, external information is required such the availability of grid congestion and renewable resources in which the signals are received in a continuous manner trying to make the consumer habit and comfort not to be influenced [25]. The effect was to try demonstrating and analyzing the potential response to demand for the different energy sectors, from home buildings to industrial buildings. Consumers, however, grew to dislike this procedure that monitors every utilization of energy [26].

Concerning the Building-to-Grid methodology, there is an intended development that would integrate the feedback in energy that would facilitate automatic responses as well as the user access being independent.  Such a feature is aimed at facilitating predictability for every single user than clustering them as in B2G. Also, the users are able to receive ancillary services that include the control of frequency and control of voltage [27]. Using B2G involves its implementation to 10 buildings that are selected and the 350kW consumption should be reduced during their peak times after the installation is complete and working [45]. The load in this model is managed for possible testing of their various characteristics with an investigation on the advantages, its ability and drawbacks in decentralized and centralized private feedback systems. The flexibility potential of the consumed energy in the various sectors is assessed to come up with better systems that can manage energy in using smart grids [28].

c)Design

The selected model for this study is the B2G model in a correlational research for the deep working of the model. Choosing this type of model was due to its ability to makes use of integration in building system automation in intelligent community software agent cooperation [29]. The operator of the smart grid interacts with the community negotiation in consumption patterns or asks for support in the emergency case. Its aspect is therefore modelled to enable the agent to locally access a world model. These voluntary consumers are recommended to be at least 10 consumers who may be or may not be in the same building. The model is developed to be having a learning process for the consumers thereby has the ability to note real-time energy consumption [30]. Using this feature, an agent is enabled to plan ahead as it identifies the typical patterns of usage, equipment’s time constants and building. The model can then test for an alternative behaviour in advance. The figure below shows how [31];

Big utility customers were clustered in groups in accordance with the certain rules in statistics like the isolation of demographic differences. Also, control groups had to be created, consideration of social spread and geography. Every proband then got fitted with smart meters for the purpose of obtaining fine-grained consumption of energy.  As in the figure below [32];

Considering the building-to-grid project, it brings back a part that was to be forgotten [33]. The human factor in that the engineers consider humans and their behaviour as a stochastic or disturbance variable. In energy systems, humans can produce remarkable local intelligence if a proper inclusion is performed in the required manner. On the other side, technology is bound to be doomed if the users involved do not understand or like it [34].

It was seen important to learn the functioning of this model during its runtime since it is impossible in the manual configuration of every field trial building. An intended target B2G is meant to be marked and physically have an oriented demand response [35].

Automatic load shifting was seen to be possible when the consumer systems were also automatic with the external parameters considered as well as the comfort range. Form these, the acts of communication interface existing between smart grid system and the consumer would be able to fulfil its smart grid objectives [36].

a)Experimental designs are designs that have control over the variables that are involved in the experimentation thereby allowing the researchers to conclude whether the variables can affect the experimentation. While correlational designs are designs that are developed to simply have no control over any variable in its experimentation hence the designer has to determine from scratch which factors would affect the design and which would not.

Experimental designs are most preferred when drawing conclusions that involve establishing or intervening possibilities of influence by some factors. Also, these designs are straightforward, basic and efficient other than being repeatable for future checking and verification. However, they are affected by human error with forceful required adherence to ethical standards. They also fail to specify the reason for an outcome. Correlational designs have easier methods of accomplishment that are less rigorous experimentally since there are reduced control groups with an existence of an independent variable viable for manipulation. The design also allows the calculation of strengths in relationships between variables. More so, the design has a pointer that is used for more detailed researches. However, the weakness is the existence of correlating relationship or the relationship may not be there.

b)Some of the ethical issues that are to be considered include;

Protection of research participants. The rights and welfare of participants that volunteer in a research are important in that there may erupt series of scandals concerning research in social sciences. No matter the outcome of the research, participants should not be reprimanded. It is therefore critical for researchers to try protecting their participants.

Participants in researchers should also be respected. The study on how people may think, in the conduct of their actions with recorded observation takes a lot form the volunteers. Anything new would be recorded as well as anything that is repeated in numerous sequences. Such scrutiny can only be encouraged when the participants are respected. Respect could be attained by the researchers not posting information that may tarnish the image of its participant.

Participants also have to be informed of the potential benefits of the research both personally and generally to the society. In addition, the researcher has to review the risks involved thereby making the participant be aware of the results of the agreement. A review of the research objectives makes the participants know when the contract may be breached and prepares one for the ultimate experimentation.

c)Groups X vs Y is more effective in performance when compared to all other groups followed by group Y vs Z and lastly group X vs Z. it is also seen that X is the greater influence In performance.

References:

[1] J. C. B. H. S. D. R. Sabina, Industrial Internet of Things: Cybermanufacturing Systems, Sydney: Springer, 2016.

[2] P. Palensky and D. Dietrich, “Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads,” IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 381-388, 2011.

[3] C. Y. M. I. M. R. A. Richard, Artificial Intelligence Applications and Innovations: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14-17, 2015, Proceedings, Sydney: Springer, 2015.

[4] H. Heinrich, Photovoltaics: System Design and Practice, Sydney: John Wiley & Sons, 2012.

[5] T. Jacopo, Peak Energy Demand and Demand Side Response, Sydney: Routledge, 2015.

[6] C. Brebbia, M. Marinov, A. Andrea and C. Safta, Energy and Sustainability IV, Sydney: WIT Press, 2013.

[7] S. Michelle, A. o. S. National and a. M. Engineering, Mathematical Sciences Research Challenges for the Next-Generation Electric Grid: Summary of a Workshop, Sydney: National Academies Press, 2015.

[8] G. Yogi and K. Frank, Handbook of Energy Efficiency and Renewable Energy, Sydney: CRC Press, 2007.

[9] M. Daphne, H. Peter, L. Victor and B. Richard, Smart Grid Applications and Developments, Sydney: Springer, 2014.

[10] T. Mini and D. John, Power System SCADA and Smart Grids, Sydney: CRC Press, 2017.

[11] B. Stuart, Smart Grids: Advanced Technologies and Solutions, Second Edition, Sydney: Taylor & Francis, CRC Press, 2017.

[12] G. Clark, The Smart Grid: Enabling Energy Efficiency and Demand Response, Sydney: The Fairmont Press, Inc., 2009.

[13] J. Lawrence, Renewable Energy Integration: Practical Management of Variability, Uncertainty, and Flexibility in Power Grids, Sydney: Academic Press, 2014.

[14] C. Cecati, C. Citro and P. Siano, “Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid,” IEEE Transactions on Sustainable Energy, vol. 2, no. 4, pp. 468-476, 2011.

[15] L. Nguyen-Thinh, D. Tien, T. Ngoc and A. Hoai, Advanced Computational Methods for Knowledge Engineering: Proceedings of the 5th International Conference on Computer Science, Applied Mathematics and Applications, ICCSAMA 2017, Sydney: Springer, 2017.

[16] A. Almeida and R. Arthur, Demand-Side Management and Electricity End-Use Efficiency, Sydney: Springer Science & Business Media, 2012.

[17] S. Ruiz-Romero, A. Colmenar-Santos, F. Mur-Pérez and A. López-Rey, “Integration of distributed generation in the power distribution network: The need for smart grid control systems, communication and equipment for a smart city — Use cases,” Renewable and Sustainable Energy Reviews, vol. 38, no. 1, pp. 223-234, 2014.

[18] D.-F. Bert, P. Boris, R. Christian, U. Dirk, S. Jens-Peter, S. Miranda and Z. Thomas, Balancing Renewable Electricity: Energy Storage, Demand Side Management, and Network Extension from an Interdisciplinary Perspective, Sydney: Springer Science & Business Media, 2012.

[19] B. Leonard and T. Olivier, Complex, Intelligent, and Software Intensive Systems: Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2017), Sydney: Springer, 2017.

[20] I. Krzysztof, Smart Grid Infrastructure & Networking, Melbourne: McGraw Hill Professional, 2012.

[21] S. IEEE, 2017 Smart Grid Conference (SGC), Melbourne: IEEE, 2017.

[22] “Voltage regulation in distribution feeders with high DG penetration: From traditional to smart,” in IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future, vol. 1, no. 3, pp. 1-7, 2018.

[23] M. Juan, C. Antonio, M. Henrik, P. Pierre and Z. Marco, Integrating Renewables in Electricity Markets: Operational Problems, Melbourne: Springer Science & Business Media, 2013.

[24] L. Kwang and E.-S. Mohamed, Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, Melbourne: John Wiley & Sons, 2008.

[25] M. Boaz, P. Jorge and S. Richard, Harnessing Renewable Energy in Electric Power Systems: “Theory, Practice, Policy”, Perth: Routledge, 2010.

[26] B. Jean-Luc, Eco-friendly Innovations in Electricity Transmission and Distribution Networks, Perth: Elsevier Science, 2014.

[27] C. Brebbia and J. Sendra, Sustainability and the City, Perth: WIT Press, 2018.

[28] K. Srinivas and R. Satyanarayana, EHT Transmission Performance Evaluation: Emerging Research and Opportunities: Emerging Research and Opportunities, Perth: IGI Global, 2018.

[29] Z. Yimin, Microgrid Load Optimization Based on Generalized Demand Side Resources, Perth: Nova Science Publisher’s, Incorporated, 2018.

[30] M. Sio, New Technologies for Smart Grid Operation, Sydney: Iop Publishing Limited, 2015.

[31] K. McBee and M. Simoes, “Utilizing a Smart Grid Monitoring System to Improve Voltage Quality of Customers,” IEEE Transactions on Smart Grid, vol. 3, no. 2, pp. 738-743, 2012.

[32] S. Bourennance, Wireless Communication And Sensor Network – Proceedings Of The International Conference (Wcsn 2015), Perth: World Scientific, 2016.

[33] H. Zhaoguang, H. Xinyang and W. Quan, Integrated Resource Strategic Planning and Power Demand-Side Management, Perth: Springer Science & Business Media, 2013.

[34] Y. Jinyue, Handbook of Clean Energy Systems, 6 Volume Set, Volume 5, Adelaide: John Wiley & Sons, 2015.

[35] G. Reinhard, H. Kai-Steffen and K. Udo, Measurement, Modelling and Evaluation of Computing Systems: 19th International GI/ITG Conference, MMB 2018, Erlangen, Germany, February 26-28, 2018, Proceedings, Adelaide: Springer, 2018.

[36] S. Takuro, K. Daniel, D. Bin, M. Martin, Z. Zhenyu, W. Jun and A. Solomon, Smart Grid Standards: Specifications, Requirements, and Technologies, Adelaide: John Wiley & Sons, 2015.

[37] Q. Meikang, Smart Computing and Communication: Second International Conference, SmartCom 2017, Shenzhen, China, December 10-12, 2017, Proceedings, Adelaide: Springer, 2018.

[38] D.-F. Bert, P. Boris, R. Christian, U. Dirk, S. Jens-Peter, S. Miranda and Z. Thomas, Balancing Renewable Electricity: Energy Storage, Demand Side Management, and Network Extension from an Interdisciplinary Perspective, Adelaide: Springer Science & Business Media, 2012.

[39] C. Brebbia, A. Marinov, A. Andrea and C. Safta, Energy and Sustainability IV, Adelaide: WIT Press, 2013.

[40] K. Mets, J. Ojea and C. Develder, “Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis,” IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1771-1796, 2014.

[41] L. Nguyen-Thinh, D. Tien, T. Ngoc and A. Hoai, Advanced Computational Methods for Knowledge Engineering: Proceedings of the 5th International Conference on Computer Science, Applied Mathematics and Applications, ICCSAMA 2017, Adelaide: Springer, 2017.

[42] J. Tooraj, The Future of Electricity Demand: Customers, Citizens and Loads, Adelaide: Cambridge University Press, 2011.

[43] T. M. and A. “. M., “A review of the development of Smart Grid technologies,” Renewable and Sustainable Energy Reviews, vol. 59, no. 1, pp. 710-725, 2016.

[44] M. Luis, M. Camarinha, A.-K. Kankam and J. Mohammad, Technological Innovation for Resilient Systems: 9th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018, Costa de Caparica, Portugal, May 2-4, 2018, Proceedings, Adelaide: Springer, 2018.

[45] H. Farag, E. El-Saadany and R. Seethapathy, “A Two Ways Communication-Based Distributed Control for Voltage Regulation in Smart Distribution Feeders,” IEEE Transactions on Smart Grid, vol. 3, no. 1, pp. 271-281, 2012.

[46] A. I. R. Management, Sustainable Practices: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications, Melbourne: IGI Global, 2013.

[47] G. Clark and C. John, Demand-Side Management: Concepts and Methods, Melbourne: Pennwell Pub, 2010.

[48] S. Colak, G. Sagiroglu, M. Fulli, Yesilbudak and C. Covrig, “A survey on the critical issues in smart grid technologies,” Renewable and Sustainable Energy Reviews, vol. 54, no. 1, pp. 396-405, 2016.

[49] A. Mohamed and M. Ali, Modeling and Simulation of Smart Grid Integrated with Hybrid Renewable Energy Systems, Melbourne: Springer International Publishing, 2017.

[50] M.-I. Behnam and J. Farkhondeh, Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs, Melbourne: Springer International Publishing, 2018.

[51] S. Jennie, W. Elizabeth and R. Tarla, Smart Grid (R)Evolution, Melbourne: Cambridge University Press, 2015.

[52] S. Hongjian, H. Nikos, P. Vincent, C. Laurence and A. Miguel, Smarter Energy: From Smart Metering to the Smart Grid, Melbourne: Institution of Engineering and Technology, 2016.

[53] F. X., M. S., X. G. and Y. D., “Smart Grid — The New and Improved Power Grid: A Survey,” IEEE Communications Surveys & Tutorials, vol. 14, no. 4, pp. 944-980, 2012.

[54] B. Gert, L. Till, M. Tobias, M. Sven-Uwe and R. Paul, Regulatory Pathways For Smart Grid Development in China, Melbourne: Springer, 2015.

[55] P. Gerro, Smart Microgrid Control: Transactive energy and intelligent control automation in power and cogenerative smart grids, Melbourne: Gerro Prinsloo, 2016.

[56] A. Ahmad, Smart Grid as a Solution for Renewable and Efficient Energy, Brisbane: IGI Global, 2016.

[57] Z. Thant, T. Nguyen, R. Shaolei and S. H. Choong, A Survey on Coordinated Power Management in Multi-Tenant Data Centers, Brisbane: Springer, 2017.

[58] Z. Yimin, Microgrid Load Optimization Based on Generalized Demand Side Resources, Brisbane: Nova Science Publisher’s, Incorporated, 2018.

[59] B. Leonard, X. Fatos, J. Nadeem, S. Evjola and K. Vladi, Advances in Internet, Data & Web Technologies: The 6th International Conference on Emerging Internet, Data & Web Technologies (EIDWT-2018), Brisbane: Springer, 2018.

[60] A. Zeineb, G. Adel and B.-B. Lazhar, Survey about Public Perception Regarding Smart Grid, Energy Efficiency & Renewable Energies Applications in Qatar, Brisbane: Elsevier Science, 2018.

[61] B. Leonard, E. Tomoya and T. Makoto, Advances in Network-Based Information Systems: The 20th International Conference on Network-Based Information Systems (NBiS-2017), Brisbane: Springer, 2017