scholarly journals Demand Response Management Algorithm for Distributed Multi-Utility Environment in Smart Grid

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Game Theory ◽  
Smart Grid ◽  
Information And Communication Technologies ◽  
Energy Management ◽  
Demand Response ◽  
Communication Technologies ◽  
Energy Market ◽  
Grid Environment ◽  
Utility Companies ◽  
Information And Communication

Effective usage of Information and Communication Technologies (ICT) has started with a paradigm shift in the energy management and functioning of the conventional power grid. It also aids in the maintenance of the complete information about consumer usage pattern, power storage, supply and regulation. Blending of information and communication technologies with energy management creates a smart grid environment which makes it move to the next horizon. The smart grid environment, uplifts renewable energy sources and brings out novel strategies in the energy market. The new functioning of the energy market attracts more utility companies for decentralized power generation and optimizes the power price for the consumer. The consumer plays an active role in the demand response modelling to maximize the welfare of the utility and to obtain the optimized price for their demand. In this paper, a novel demand response management scheme is proposed for multi-utility environment. The utility companies function in a peer to peer manner to communicate effectively and to select a specific utility from a set of utilities for the power supply. The selection of single utility is based on a non-cooperative game theory algorithm where the demand and generated power should be balanced to maximize the welfare of the utility and the residential consumers. The power price can be updated in an equal interval to allow all the utilities to participate in the Distributed Multi-Utility Demand Response Management (DMDRM) system. The simulated results justify that the distributed noncooperative game theory algorithm certainly maximizes the welfare of the utility companies and residential consumers.

scholarly journals System-Theoretic Process Analysis (STPA) for Hazard Analysis in Complex Systems: The Case of “Demand-Side Management in a Smart Grid”

Systems ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 33 ◽  
Author(s):  
Stylianos Karatzas ◽  
Athanasios Chassiakos
Keyword(s):  
Smart Grid ◽  
Complex Systems ◽  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Energy Market ◽  
System Modelling ◽  
Home Energy Management

Inelasticity of demand along with the distributed energy sources and energy market democratization pose significant challenges which have considerable negative impacts on overall grid balance. The need for increased capacity and flexibility in the era of energy market digitalization has introduced new requirements in the energy supply network which could not be satisfied without continuous and costly local power network upgrades. Additionally, with the emergence of Smart Homes (SHs) and Home Energy Management (HEM) systems for monitoring and operating household appliances, opportunities have arisen for automated Demand Response (DR). DR is exploited for the modification of the consumer energy demand, in response to the specific conditions within the electricity system (e.g., peak period network congestion). In order to optimally integrate DR in the broader Smart Grid (SG) system, modelling of the system parameters and safety analysis is required. In this paper, the implementation of STPA (System-Theoretic Process Analysis) structured method, as a relatively new hazard analysis technique for complex systems is presented and the feasibility of STPA implementation for loss prevention on a Demand Response system for home energy management, and within the complex SG context, is examined. The applied method delivers a mechanism useful in understanding where gaps in current operational risk structures may exist. The STPA findings in terms of loss scenarios can be used to generate a variety of safeguards to ensure secure operational control and in implementing targeted strategies through standard approaches of risk assessment.

scholarly journals A survey on smart grid communication system

2015 ◽  
Vol 4 ◽  
Author(s):  
Miles H.F. Wen ◽  
Ka-Cheong Leung ◽  
Victor O.K. Li ◽  
Xingze He ◽  
C.-C. Jay Kuo
Keyword(s):  
Smart Grid ◽  
Information And Communication Technologies ◽  
Communication System ◽  
Communication Systems ◽  
Communication Technologies ◽  
Power Electronic ◽  
Recent Developments ◽  
Information And Communication ◽  
Smart Grid Communication ◽  
Generation Power

Concerns with global warming prompted many governments to mandate increased proportion of electricity generation from renewable sources. This, together with the desire to have more efficient and secure power generation and distribution, has driven research in the next-generation power grid, namely, the smart grid. Through integrating advanced information and communication technologies with power electronic and electric power technologies, smart grid will be highly reliable, efficient, and environmental-friendly. A key component of smart grid is the communication system. This paper explores the design goals and functions of the smart grid communication system, followed by an in-depth investigation on the communication requirements. Discussions on some of the recent developments related to smart grid communication systems are also introduced.

scholarly journals From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1456 ◽  
Author(s):  
Vangelis Marinakis ◽  
Haris Doukas ◽  
Konstantinos Koasidis ◽  
Hanan Albuflasa
Keyword(s):  
Information And Communication Technologies ◽  
Energy Management ◽  
Energy Savings ◽  
Communication Technologies ◽  
Energy System ◽  
Effective Energy ◽  
Behavioural Patterns ◽  
Information And Communication ◽  
Regional Authorities

The transition of the energy system into a more efficient state requires innovative ideas to finance new schemes and engage people into adjusting their behavioural patterns concerning consumption. Effective energy management combined with Information and Communication Technologies (ICTs) open new opportunities for local and regional authorities, but also for energy suppliers, utilities and other obligated parties, or even energy cooperatives, to implement mechanisms that allow people to become more efficient either by producing and trading energy or by reducing their energy consumption. In this paper, a novel framework is proposed connecting energy savings with a digital energy currency. This framework builds reward schemes where the energy end-users could benefit financially from saving energy, by receiving coins according to their real consumption compared to the predicted consumption if no actions were to take place. A pilot appraisal of such a scheme is presented for the case of Bahrain, so as to simulate the behaviour of the proposed framework in order for it to become a viable choice for intelligent energy management in future action plans.

scholarly journals Transition towards Sustainable Energy

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Renewable Energy ◽  
Information And Communication Technologies ◽  
Demand Response ◽  
Smart Grids ◽  
Renewable Energy Sources ◽  
Communication Technologies ◽  
Power Grids ◽  
Energy Potential ◽  
Information And Communication ◽  
Demand Response Programs

The presentation describes the comparison of energy consumption and generation between Germany and Indonesia. It shows then the potential of renewable energy potential in Indonesia and the estimation of their utilization by 2030. The introduction of renewable energy sources in power grids requires the integration of information and communication technologies. The presentation explains the integration through smart grids and demand response programs.

scholarly journals Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 431 ◽  
Author(s):  
Mahnoor Khan ◽  
Nadeem Javaid ◽  
Sajjad ◽  
Abdullah ◽  
Adnan Naseem ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Demand Response ◽  
Load Forecasting ◽  
Short Term ◽  
Priority Index ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Short Term Load Forecasting ◽  
Utility Companies

Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.

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