Demand Side Management Mechanism Research Articles

Methodology to optimise electricity demand in the residential sector through efficient load management

The current energy model requires a transformation based on a sustainable model that is accessible to all, focused on the needs of citizens and committed to climate change. Moreover, the rising cost of non-renewable energy sources, and the growing presence of distributed renewable generation, has a major impact on the electricity sector, creating the need to develop mechanisms to help manage energy demand in order to converge towards an efficient, emission-free electricity system that is responsible for the environment and future generations. The main motivation is the recent change in the regulation of electricity tariff systems and the introduction of time discriminations in the residential sector. Based on the previous need and supported by a survey to find out society's predisposition and sensitivity to demand management, this paper presents the methodology developed as an active demand-side management mechanism for residential consumers, considering their load shifting priorities and prioritising the use of renewable energy if possible, reducing grid energy consumption. In a nutshell, the use of a tool capable of helping consumers to regulate their energy consumption, analyse their consumption pattern and plan their domestic loads to obtain the lowest energy and economic impact is developed. Key words. Active demand-side management, Load shedding, Energy optimisation, Renewable energy, Load prioritisation

Dynamic Time- and Load-Based Preference toward Optimal Appliance Scheduling in a Smart Home

In this paper, the household appliance scheduling based on the user predefined preferences is addressed. Previous works generally deal with this problem without integration of renewable energy sources (RESs) in smart home. The present paper proposes a new demand side management (DSM) technique considering time-varying appliance preferences and solar panel generation. The branch and bound (B&B) algorithm is developed based on three postulations that allow the time-varying preferences to be quantified in terms of time- and load-based features. Based on the input data including the load’s power rating, the absolute comfort derived from time- and load-preferences, the total energy available from the solar panels as well as the energy purchased from the utility grid, the (B&B) algorithm is run to generate the optimal energy consumption model that would give maximum comfort to the householder based on the mixed-integer linear programming (MILP) technique. To test the performance of the proposed mechanism, three scenarios are considered with local energy production and limited budget for purchasing the energy from the utility grid to cover the user needs. The simulation results reveal that the proposed DSM mechanism based on the MILP method offers maximum level of comfort for all the scenarios within the available energy limitation.

Demand-side management mechanisms in industry

Intensive development of technologies that increase energy efficiency makes the global and the Russian industry consider the introduction of demand-side management mechanisms. Though demand-side management technologies in Russia are at the early stages of introduc tion, existing mechanisms of electricity and natural gas supply allow consumers to reduce their costs and result in equalization of the demand for energy across the entire energy system. The research objective is to explore the energy tariffs in the wholesale and retail markets and mecha nisms for supplying natural gas by regional contractors as well as using the commodity exchange. The authors design mechanisms for industrial consumers to choose the most efficient options to pay for energy resources using the criterion of demand-side management. The research method ology includes the theory of industrial markets and a problem-oriented approach. The authors apply analysis, synthesis, system analysis, statistical methods. Yielded results of the research into programmes on demand-side management administered in different countries enable the authors to prove that the most efficient direction for demand-side management development in Russia is economic norm setting. Within this direction the authors develop algorithms and decision-making matrices for choosing the most efficient tariff option to buy electricity and natural gas by industrial consumers. The researchers evidence the possibility of implement ing demand-side management on energy markets of Russia and provide methodological and organisational support. The findings can be helpful for industrial enterprises and government agencies developing and running their energy efficiency improvement programs.

Energy Management of Microgrids Using Load Shifting and Multi-agent System

The demand-side management (DSM) has a great significance as it covers economical activities through decreasing demand of electricity during high consumption hours. Meanwhile, the multi-agent system (MAS) is a computational intelligence technique that has been applied to reduce the overall consumers’ cost as well as total loads. This paper proposes a hybrid technique based on DSM and MAS to manage the energy in residential, commercial, and industrial microgrids. First, the DSM mechanism based on load shifting technique for managing and coordinating load demand is implemented. Then, according to the resulting scheduled load curve, the MAS is utilized in order to optimally coordinate different renewable sources to cover the demand and allowable energy to exchange between the main grid and microgrids. The problem is formulated as an optimization problem and solved by a powerful Antlion optimizer algorithm with an objective of maximization of load factor and minimization of overall consumers’ cost. The validation of the proposed algorithm is achieved via comparative studies with other published works. The results show the prosperity of the proposed technique and its capability of saving energy cost and reducing peak demands.

Robust Optimization of Demand Response Power Bids for Drinking Water Systems

The development of smart grids represents a major breakthrough in the management of electric power and drinking water systems. On the one hand, smart grids have contributed to the development of energy efficiency and demand side management mechanisms such as Demand Response, making it possible to reduce peak load and adapt elastic demand to fluctuation generation. On the other hand, smart water networks and sophisticated Supervisory Control and Data Acquisition systems in the water industry have allowed one to optimize, control and monitor the water flow throughout its entire process. Being a highly energy intensive industry and having an electrical flexibility by the presence of storage elements such as tanks, drinking water systems have the ability to address energy efficiency mechanisms such as Demand Response. In this paper, the French demand response mechanism in spot power markets is presented. Then, a chance constrained problem is formulated to integrate water systems flexibility to power system operation, under water demand uncertainties. Numerical results are discussed based on a real water system in France, demonstrating the relevance of the approach in terms of financial benefits and risk management.

Comparing the status and challenges of demand-side management (DSM) implementation in Asia-Pacific region: a case study of China’s power sector

Demand Side Management (DSM) performs a significant function in the electrically powered energy system in order to manage power demand-side resources. Although China has brought DSM for the first time in the early 1990s, its advantages have not been entirely utilized. In this study, we first present a quick history of the energy policy of China, its electricity enterprise, and reform. Based on the analysis of DSM practices in Asia- Pacific region, the DSM development and the reform of electrical energy pricing in seven countries namely, Pakistan, India, Malaysia, Singapore, Thailand, Bangladesh, and China have been discussed. The primary limitations to DSM include fallacious market-oriented DSM mechanisms, the underdeveloped power carrier industry, low motivation level of grid companies for DSM because of their conventional business model, and low motivation of electricity end-users. This paper represents a case study of China’s electricity sector reform which can probably alter power transmission and pricing of distribution scheme and enterprise model of grid companies, thereby enhancing competition. A number of rules & regulations and series of policies linked to DSM were formulated by Chinese country wide ministries. But still, there are many troubles in the implementation of DSM. The current status of DSM improvement in China is investigated from distinct factors of policies and guidelines associated with DSM, DSM improvement of representative regions and DSM organizational administration. This study has also discussed the challenges faced by the implementation of DSM and proposed feasible measures totally based on the challenges encountered in implementing of DSM. This study is beneficial in promoting the improvement of China’s DSM. Based on these findings, the possible effects of DSM reforms are; government must give more attention to DSM; grid companies might also have better incentives to invest in DSM; DSM end users of electricity motivations may also increase; demand response applications can be improved and commercial DSM business models can be proposed.

Demand-Side Management with a State Space Consideration

Power networks are gateways to transfer power from generators to end-users. Often, it is assumed that the transfer occurs freely without any limiting factors. However, power flows over a network can be limited by predetermined limits that may come from physical reasons, such as line capacity or Kirchhoff’s laws. When flow is constrained by these limits, this is called congestion, which reduces the energy efficiency and splits the price for electricity across the congested lines. One promising, cost-effective way to relieve the impact of the congestion is demand-side management (DSM). However, it is unclear how much DSM can impact congestion and where it can control the demand. This paper proposes a new DSM mechanism based on locational willingness-to-pay (WTP) centered around income statistics; utilizes a state-space tool to determine the possibility to alter prices by DSM; and formulates a convex optimization problem to decide the DSM. The proposed methodology is tested on IEEE (Institute of Electrical and Electronics Engineers) systems with two commonly used objectives: cost minimization and social welfare maximization.

Demand side management in urban district heating networks

This paper proposes a realistic demand side management mechanism in an urban district heating network (DHN) to improve system efficiency and manage congestion issues. Comprehensive models including the circulating pump, the distribution network, the building space heating (SH) and domestic hot water (DHW) demand were employed to support day-ahead hourly energy schedule optimization for district heating substations. Flexibility in both SH and DHW were fully exploited and the impacts of both weekly pattern and building type were modelled and identified in detail. The energy consumption scheduling problem was formulated for both the individual substations and the district heating operator. Three main features were considered in the formulation: user comfort, the heat market and network congestion. A case study was performed based on a representative urban DHN with a 3.5 MW peak thermal load including both residential and commercial buildings. Results show an up to 11% reduction of energy costs. A sensitivity analysis was conducted which provides decision makers with insights into how sensitive the optimum solution is to any changes in energy, user comfort or pumping costs.

Green Energy Scheduling for Demand Side Management in the Smart Grid

Demand side management (DSM) is an essential property of smart grid systems. Along with increasing expectations related to power quality from customers, and as new types of loads emerge, such as electric vehicles, local (renewable) energy generation, and stationary and mobile energy storage, it is critical to develop new methods for DSM. In this paper, we first construct a more efficient and reliable communication infrastructure in smart grid based on cognitive radio technology, which is an essential component for enabling DSM. Then, we propose a distributed energy storage planning approach based on game algorithm in DSM, which helps users select the appropriate size of storage units for balancing the cost in the planning period and during its use. Since planning problems may lead to consumer discomfort, we propose a cost function consisting of the billing, generation costs, and discomfort costs to balance users’ preferences with the payment. Furthermore, a game theory-based distributed energy management scheme is developed in DSM without leaking user privacy, which is used as inner optimization in our proposed distributed energy storage planning approach. In this energy management scheme, Nash equilibrium is obtained with minimum information exchange using proximal decomposition algorithm. Simulation results show superior performance of our proposed DSM mechanism in reducing the peak-to-average ratio, total cost, user’s daily payment, and energy consumption in smart grid communication networks.

An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.

Increasing the energy efficiency of warehouses using demand-side management mechanisms

The complex approach and expansion of the functionality of DSM programs is offered due to the application of services and criteria for classification of warehouses in terms of energy efficiency. When analyzing the effectiveness of the application of DSM programs, namely the optimality of power consumption modes, it is suggested to assess the power supply mode during the entire technological period. Considering the electrical supply of warehouses as an electricity supply to the consumer with sharply variable nonlinear characteristics, the power of which is supplied from the generators, the operating voltage and current values of which are variable, it is proposed to use the modified Frieze inactive power index. The use of the Q F index is advisable, since it will be located even in the absence of reactive elements in the consumer system. The influence of the consumption mode on the first harmonic is analyzed. The received characteristics are caused by presence of influence of consumption mode on the general characteristics of electricity supply mode optimality. The analysis of higher harmonics can also be used to assess the effect of unevenness. Such analysis requires additional studies, but it can be argued that the use of the Frieze inactive power index allows to assess the degree of uneven electricity consumption in accordance with a given optimal level.

Demand-side management (DSM) in the context of China's on-going power sector reform

As an approach to manage power demand-side resources, DSM plays an important role in electric power system. Though DSM was introduced into China in the early 1990s, its benefits have been underutilized. Using literature study, interview methods and three data sources, this paper examines the likely impacts of China's on-going power sector reform on its DSM. It finds that the major constraints to DSM in China are the insufficient and improper market-based DSM mechanism, grid companies’ low motivations for DSM due to their traditional business model, the underdeveloped energy service industry, and electricity end-users’ low motivation for DSM. China's on-going power sector reform will change power transmission and distribution pricing and grid companies’ business model and introduce competition into retail-side. Drawing on these findings, it is concluded that the likely impacts of the new reform on DSM are: governments may attach more importance to DSM; grid companies may have more motivations for DSM investment; electricity end-users’ motivations for DSM may be both enhanced and dampened; electricity retailers’ motivations for DSM may be dampened; demand response application may be enhanced, and more DSM business models may be developed. Finally, policy implications are provided.

Performance evaluation of power demand scheduling scenarios in a smart grid environment

Smart grid technology is considered as the ultimate solution to challenges that emerge from the increasing power demands, the subsequent increase in pollution, and the outmoded power grid infrastructure. The successful implementation of the smart grid is mainly driven by the utilization of modern communication technologies, which aim at the provision of advanced demand side management mechanisms, such as demand response. In this paper, we present and analyze four power-demand scheduling scenarios that aim to reduce the peak demand in a smart grid infrastructure. The proposed scenarios consider that each consumer is equipped with a certain number of appliances of different power demands and different operational times, while the percentage of consumers that agree to participate in the demand scheduling program is also incorporated in our models. We provide the analysis for the determination of the peak demand in a residential area, based on recursive formulas. The proposed analysis is validated through simulations; the accuracy of the analytical models is found to be quite satisfactory. Moreover, we unveil the consistency and necessity of the proposed scenarios and corresponding analytical models.

Analysis of control strategies for thermally activated building systems under demand side management mechanisms

Thermally activated buildings systems (TABS) are systems that integrate heating/cooling devices in the building structure, so that the building elements act as thermal storage and have an active role in the energy supply and demand management. Although TABS are well known systems, there are still open questions in their realization, mainly concerning appropriate control strategies which are influenced by the large thermal inertia. The purpose of this paper is to analyze the influence of demand side management control strategies on the performance of a thermally activated building system applied in a commercial building. The goal is to estimate the potential of TABS for load shifting requested by the electricity grid. The analysis is performed by means of a sample case: first the existing TABS control strategy and then the possible implementation of DSM mechanisms are analyzed. In particular three different demand side management mechanisms are evaluated: (i) a peak shaving strategy, (ii) a random request of switching on/off the system and (iii) a night load shifting strategy. The simulation results show high potential of TABS within the DSM framework, since TABS allow load control while scarcely affect thermal comfort.

Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid

The electric energy distribution infrastructure is undergoing a startling technological evolution with the development of the smart grid concept, which allows more interaction between the supply- and the demand-side of the network and results in a great optimization potential. In this paper, we focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy source and/or energy storage device. By means of a day-ahead demand-side management mechanism regulated through an independent central unit, the latter users are interested in reducing their monetary expense by producing or storing energy rather than just purchasing their energy needs from the grid. Using a general energy pricing model, we tackle the grid optimization design from two different perspectives: a user-oriented optimization and an holistic-based design. In the former case, we optimize each user individually by formulating the grid optimization problem as a noncooperative game, whose solution analysis is addressed building on the theory of variational inequalities. In the latter case, we focus instead on the joint optimization of the whole system, allowing some cooperation among the users. For both formulations, we devise distributed and iterative algorithms providing the optimal production/storage strategies of the users, along with their convergence properties. Among all, the proposed algorithms preserve the users' privacy and require very limited signaling with the central unit.

Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan

This study explores the inter-relationships among economy, energy and CO 2 emissions of 37 industrial sectors in Taiwan in order to provide insight regarding sustainable development policy making. Grey relation analysis was used to analyse the productivity, aggregate energy consumption, and the use of fuel mix (electricity, coal, oil and gas) in relation to CO 2 emission changes. An innovative evaluative index system was devised to explore grey relation grades among economics, energy and environmental quality. Results indicate that a rapid increase in electricity generation during the past 10 years is the main reason for CO 2 emission increase in Taiwan. The largest CO 2 emitting sectors include iron and steel, transportation, petrochemical materials, commerce and other services. Therefore, it is important to reduce the energy intensity of these sectors by energy conservation, efficiency improvement and adjustment of industrial structure towards high value-added products and services. Economic growth for all industries has a more significant influence, than does total energy consumption, on CO 2 emission increase in Taiwan. It is also important to decouple the energy consumption and production to reduce the impacts of CO 2 on economic growth. Furthermore, most of the sectors examined had increased CO 2 emissions, except for machinery and road transportation. For high energy intensive and CO 2 intensive industries, governmental policies for CO 2 mitigation should be directed towards low carbon fuels as well as towards enhancement of the demand side management mechanism, without loss of the nation's competitiveness.