Dynamic demand response solution for industrial customers

Electric demand side management (DSM) focuses on changing the electricity consumption patterns of end-use customers through improving energy efficiency and optimizing allocation of power. Demand response (DR) is a DSM solution that targets residential, commercial and industrial customers, and is developed for demand reduction or demand shifting at a specific time for a specific duration. In the absence of on-site generation or possibility of demand shifting, consumption level needs to be lowered. While non-criticality of loads at the residential and commercial levels allows for demand reduction with relative ease, demand reduction of industrial processes requires a more sophisticated solution. Production constraints, inventory constraints, maintenance schedules and crew management are some of the many factors that have to be taken into account before one or more processes can be temporarily shut down. An intelligent system is designed in this paper for implementation of DR at an industrial site. Based on the various operational constraints of the industrial process, it determines the loads that could be potentially curtailed. Fuzzy/expert systems are used to derive a priority factor for different candidate loads. This information can then be used by the plant operator/DR client to make a comply/opt-out decision during a utility-initiated DR event.

Per the National Energy Policy, Demand Side Management (DSM) is one of the energy conservations that performs a function to manage electric power of demand-side resources. One of the DSM solutions is a demand response program, which is a part of Thailand Smart Grid Action Plan 2017 - 2021. Demand response program such as peak demand reduction plays a role in both the management of the electricity crisis and enhance energy security. This paper presents a pilot project for a fully automated demand response program at MEA Rat Burana District Office. The system is composed of a Building Energy Management System (BEMS) with Demand Response Client gateway and 5 energy controllers at the air conditioner by using the OpenADR2.0b protocol. Also, this concept leads to automatic or semi-automatic demand response program in the future. The result shows the total energy consumption reduction for air conditioners by 53.5%. The future works to be carried out are to implement into other MEA District Office such as Khlong Toei, Yan Nawa and Bang Khun Thian and to test with a Load Aggregator Management System (LAMS).

In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme’s optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently.

With a significant deployment of smart meters across end-user platforms, the dynamic visibility of energy flow among the end-users has been increased significantly. The granular information of smart meters can be used to improve the load forecast accuracy and to influence energy consumption patterns with demand side management (DSM) schemes. This paper addresses the challenges of smart meter data size, complexity, variability and volatility for efficient use in load forecast and DSM. A novel clustering-based approach for analysis of smart meter data, aimed at more accurate and detailed load profiling, reduced profile complexity, improved load forecast accuracy and providing optimal DSM solutions is proposed. The proposed approach utilizes an advanced clustering algorithm to reduce the data size. The approach addresses data complexity, variability and volatility by linearizing the load profiles and minimizing the errors. The validity of the approach is demonstrated on an Irish smart meter dataset and on a simulated solar photovoltaic (PV) data and showed an improved load forecast accuracy, improved DSM solutions, and reduced computational burden. The improvements in the DSM solution are evidenced by a higher cost saving with a higher peak load reduction at the lower level of demand flexibility.

Electricity demand-side management (DSM) refers to technologies, actions and programmes on the demand-side of electric meters that seek to manage or decrease electricity consumption, in order to reduce overall electricity system expenditures or contribute to the achievement of policy objectives such as carbon emissions reduction or balancing electricity supply and demand. The programme and policy side of DSM has been little explored and the research contributes to filling this gap by undertaking a global systematic review of electricity-focussed DSM programme case studies to determine what constitutes best practice programmes and policies in DSM. Systematic Reviews are a method primarily used in the medical sciences and to which have had limited application in the energy policy field to date. DSM 'best practice' is defined as programmes and policies that reduce overall electricity demand or shift loads to off-peak time periods, through measures that are environmentally, economically and socially beneficial, and are transferable between regions and countries. Best practices are not static but constantly evolving as circumstances change and new evidence is brought to light. The UK is currently consulting on how to include the demand- side in its Electricity Market Reform policy proposals and a key outcome of this research is to determine the transferability of international best practices to the UK to feed into this process. The research aims to determine how and why particular DSM programmes are successful or fail and what the underlying contextual factors are that could prevent or allow their transferability to the UK. This paper focuses on the findings from an extensive year-long literature review and some of the preliminary results from the Systematic Review.

A semi-distributed electric demand-side management system with PV generation for self-consumption enhancement

Segmentation analysis of residential water-electricity demand for customized demand-side management programs

Social license to automate: A critical review of emerging approaches to electricity demand management

Designing an optimal Demand Response (DR) program necessitates having information on the utility of users. However, that is not easily accessible since the users may not be willing to report their personal information. In this paper, we propose an incentive-based DR mechanism for Commercial and Industrial (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I) customers, leveraging contract theory to address the challenges of incomplete information and ensure the participation of customers in the DR program. Designing a DR mechanism includes specifying an incentive reward, penalty, and the corresponding demand reduction as functions of the customer’s utility information. These functions are obtained through an optimization problem that maximizes the grid operator’s profit, ensures truthful reporting of personal information and participation of the customers in the DR program. After formulating the optimal mechanism, the main technical challenge is reformulating the obtained non-convex and computationally inefficient optimization problem to a tractable and convex one. The extensive numerical evaluations demonstrate the effectiveness of our proposed framework in comparison to existing benchmark schemes. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Imbalances between supply and demand, particularly during the hot summer months, can result in issues such as power outages. This problem is detrimental for both grid operators and customers alike. DR programs have been implemented in numerous countries to address these challenges. C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I customers, due to the schedulable nature of their loads, make for ideal targets for these programs. However, the main obstacle to implementing such programs is customer participation. Incentive rewards offered in these programs for load reduction are fixed for all customers, discouraging those with low rewards from participating and imposing high costs on grid operators for those with high rewards. On the other side, designing unique rewards and penalties for each customer necessitates information about their preferences, which grid operators cannot access. In this paper, we propose an incentive mechanism based on the contract between the grid operator and C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\&amp;$</tex-math> </inline-formula> I customers that ensures customers truthfully report their preferences while simultaneously maximizing grid operator profit and guaranteeing the participation of all customers. The proposed DR program can be implemented for large non-residential customers in order to reduce the peak load of the grid.

Demand side management (DSM) is considered as a new facet in Indian power industry to solve the problems of power shortage and energy shortage. Load management or rather peak power management is the only focus of DSM programs in Indian utilities. "Load shedding" is the main tool for load management with the utility in Maharashtra state. DSM in its true sense is yet to be implemented in Indian power market. This paper defines and describes two new indices which quantify the technical and economic benefits of DSM programs. These indices would help understand the rationale for a particular DSM program for engineers, for economists and for management personnel

Advanced metering infrastructure (AMI) is an enabling technology improving the quality of metering and transferring the energy consumption data in the electric energy network. Data with such quality acquired from a bidirectional infrastructure is a highly instrumental in increasing energy awareness in smart cities. The bidirectional communication infrastructure provided by AMI increases the customers' awareness about their consumption behavior, too. One of the issues affected by considering AMI in distribution network is demand side management (DSM). The accurate customer characterization using AMI data is a practical point in increasing energy awareness in the smart cities. The role of AMI in DSM programs is embossed when customers are regarded as influencing participants in these programs. This paper proposes a procedure investigating the role of AMI in improving demand response (DR) by costumer characterization in distribution network. Authors believe that DR is suitably implemented in distribution network if the analysis of customers' consumption behavior is conducted accurately. Besides, customers' capacities for participating in DR are different which is unfold by analyzing their consumption behaviors. Customers' consumption that are recorded by smart meters in different time slots compose time series on which pattern recognition procedures are applied. Different time series are clustered on the basis of their similarities and DR is implemented on each cluster separately. In This study, DR is implemented in two years in one of which capacities of each cluster for participation in DR is estimated and in another one these capacities are compared with the observed capacities. The results of this study confirm the accuracy of the estimated capacities and the proposed method. This knowledge helps the networks' operators to get a more accurate overview on the total capacity of the distribution network for implementing DR. The proposed method is implemented on a real dataset provided by Irish social science data archive (ISSDA).

Flexible load response allows to manage local power consumption in response to supply conditions, such as high market price, peak demand or regulation signal, thus, lower grid operating costs, increased system reliability and improved energy efficiency can be achieved. Besides, demand response becomes large contributor to overall building expense reduction, especially in large grid-congested cities. HVAC system in commercial building accounts for a huge amount of power consumption and greatly contributes to the peak load. In this work, we propose an economic model predictive controller based on a thermal dynamic building model that discussed in our previous work. Simulation results show that the proposed controller can effectively and optimally control the HVAC system to utilize the time-varying electricity prices and the future disturbances to minimize the electricity costs, significantly reduce the peak demand and increase energy saving and efficiency, while respecting the comfort level.

Demand Side Management (DSM) in electricity market is a tool that can improved energy efficiency as well as achieve socio-economic and environmental targets through controlled consumption. For the past two decade or more, DSM programs have registered significant results for many countries. However, detailed analysis of its real impact as observed by a large number of pilot studies suggest that such programs need to be fine-tuned to suit clearly identified energy mix conditions in different countries. This article aims to provide recommendations for the policy instruments to be used for prompt demand response with a view to maximizing energy and environmental efficiencies of The Gambia. This article suggests a DSM pilot model program that can be used in the generation mix of the country. Beside the natural increase electricity access benefits from IPPs and cross border interconnection, DSM’s fully implementation will improve the flexibility and reliability of the Gambia energy system. In recent years, we show fast increase demand for electricity access but a slow response in meeting generation capacity, simple DSM tools can be used to solve this ordeal

In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

This paper reviews the relationship between energy efficiency and demand response and discusses approaches and barriers to coordinating energy efficiency and demand response. The paper is intended to support the 10 implementation goals of the National Action Plan for Energy Efficiency's Vision to achieve all cost-effective energy efficiency by 2025. Improving energy efficiency in our homes, businesses, schools, governments, and industries - which consume more than 70 percent of the nation's natural gas and electricity - is one of the most constructive, cost-effective ways to address the challenges of high energy prices, energy security and independence, air pollution, and global climate change. While energy efficiency is an increasingly prominent component of efforts to supply affordable, reliable, secure, and clean electric power, demand response is becoming a valuable tool in utility and regional resource plans. The Federal Energy Regulatory Commission (FERC) estimated the contribution from existing U.S. demand response resources at about 41,000 megawatts (MW), about 5.8 percent of 2008 summer peak demand (FERC, 2008). Moreover, FERC recently estimated nationwide achievable demand response potential at 138,000 MW (14 percent of peak demand) by 2019 (FERC, 2009).2 A recent Electric Power Research Institute study estimates that 'the combination of demand response and energy efficiency programs has the potential to reduce non-coincident summer peak demand by 157 GW' by 2030, or 14-20 percent below projected levels (EPRI, 2009a). This paper supports the Action Plan's effort to coordinate energy efficiency and demand response programs to maximize value to customers. For information on the full suite of policy and programmatic options for removing barriers to energy efficiency, see the Vision for 2025 and the various other Action Plan papers and guides available at www.epa.gov/eeactionplan.