Masood Parvania, Mahmud Fotuhi‐Firuzabad, Mohammad Shahidehpour
Advancements in smart grid technologies have made it possible to apply various options and strategies for the optimization of demand response (DR) in electricity markets. DR aggregation would accumulate potential DR schedules and constraints offered by small- and medium-sized customers for the participation in wholesale electricity markets. Despite various advantages offered by the hourly DR in electricity markets, practical market tools that can optimize the economic options available to DR aggregators and market participants are not readily attainable. In this context, this paper presents an optimization framework for the DR aggregation in wholesale electricity markets. The proposed study focuses on the modeling strategies for energy markets. In the proposed model, DR aggregators offer customers various contracts for load curtailment, load shifting, utilization of onsite generation, and energy storage systems as possible strategies for hourly load reductions. The aggregation of DR contracts is considered in the proposed price-based self-scheduling optimization model to determine optimal DR schedules for participants in day-ahead energy markets. The proposed model is examined on a sample DR aggregator and the numerical results are discussed in the paper.
With the present residential electricity usage pattern, the power grid is not efficiently utilized. To avoid the need of grid expansion as a result of increasing power demand, a framework that works on behalf of both power utilities and consumers is needed. Such a framework consisting of a trustworthy adaptable smart home energy system and electricity contracts is presented. The system adapts energy usage in the home according to factors such as electricity prices, electricity contracts, and electricity usage control requests from the power utilities. The proposed contract defines pricing elements as well as the well-being of the consumer, comprising comfort and freedom to use electricity.
Smart grids enable a two-way energy demand response capability through which a utility company offers its industrial customers various call options for energy load curtailment. If a customer has the capability to accurately determine whether to accept an offer or not, then in the case of accepting an offer, the customer can earn both an option premium to participate, and a strike price for load curtailments if requested. However, today most manufacturing companies lack the capability to make the correct contract decisions for given offers. This paper proposes a novel decision model based on activity-based costing (ABC) and stochastic programming, developed to accurately evaluate the impact of load curtailments and determine as to whether or not to accept an energy load curtailment offer. The proposed model specifically targets state-transition flexible and Quality-of-Service (QoS) flexible energy use activities to reduce the peak energy demand rate. An illustrative example with the proposed decision model under a call-option based energy demand response scenario is presented. As shown from the example results, the proposed decision model can be used with emerging smart grid opportunities to provide a competitive advantage to the manufacturing industry.
Decentralized or distributed small renewable power facilities are usually installed in local communities for households and small business companies. These facilities include solar PV, concentrated solar power, and wind power, etc. In order to promote installations of such facilities, governments in many countries have developed a number of policies and business models. For example, in Germany and Canada, electricity feed-in tariff policy and business model were developed; in the USA, tax rebate policies and relevant business models were promoted. These policies and models have in some but not in large scale promoted decentralized small renewable power in local communities. The key issue is that these policies and business models do not provide sufficient incentives to local distribution companies (LDC), nor to renewable power installers and users. This paper’s research covers the creation of a business and communication model, named as LDC model, to incentivize both renewable power installers/users and LDCs. This LDC model can play a key role in promoting decentralized small-scale generation (DSG) with renewable energy in local communities. The core element of the LDC model is a revenue model which serves as an instrument to finance renewable installations for households and small commercial businesses. A case study is undertaken with real data of a power distribution company in Toronto, Canada. This paper concludes that with appropriate government policy and with the development of customized information systems for accessing households and small business via internet, an LDC will be able to take leadership in investing and installing small renewable power, and consequently enlarge the share of renewable energy supply in its local power distribution network.
The important energy requirements for the desalination process impose especially in autonomous and decentralized plants supplied by Renewable Energy Sources (RES). In this paper, five alternative energy generation topologies of Reverse Osmosis desalination process are evaluated. The proposed topologies assessed in terms of economic, environmental, technological and societal indices are compared using multi-criteria analysis, namely the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE). Ranking of topologies resulted in the selection of direct connection and hybrid configuration as optimum solutions. In case economic priorities prevail diesel generation should also be considered.
Liang Yu, Tao Jiang, Yang Cao, Shiyong Yang · 5 authors
In Internet Data Center (IDC) operations, some uncertainties are needed to be managed. Otherwise, IDC operators will be faced with a high operation risk. In existing work, electricity forward contract is adopted to minimize the IDC operation risk in smart grid environment under the assumption that workload and spot electricity price are independent. However, the above assumption is unreasonable in smart grid environment, resulting in the unsatisfactory performance of electricity forward contract. In this paper, a novel scheme is proposed to minimize the IDC operation risk in smart grid environment considering the correlation between workload and spot electricity price, based on a portfolio consisting of electricity forward contracts and electricity call (put) options in electricity derivative markets. Simulation results show that the proposed scheme can significantly reduce the IDC operation risk. Compared with electricity forward contract, the proposed scheme has more stable and better performance.
Suraj Baral, Suman Budhathoki, Hari Prasad Neopane
This paper review different policies of on-grid and off-grid rural electrification in Nepal which are imposed by two different organizations, namely, Nepal Electricity Authority and Alternative Energy Promotion Centre. Also, the paper identifies different issues in rural electrification in changing context and different initiatives taken so far on connection of micro hydropower and mini grid development. The study reveals that grid connection and mini grid initiatives are not internalized and owned by the policy and institutional mechanism. Policy and institutional mechanisms need to be revised and restructured so as to adopt mentioned initiatives and to introduce synergy effects which are not often realized because of parallel policy and institutional mechanism. The policy and institutional mechanisms should develop suitable financing/investment mechanism not only based on subsidy but also on the principle of private sector involvement, business operation modality especially trading mechanism, technology transfer and capacity building, and institutional structure of mini grid based on decentralization and liberalization which could encourage competition in the sector as whole. Such policy provision and institutional mechanism make the flow of financial resource from urban to rural and also retain the human capital in the local level.
Many electricity markets around the world treat electricity as a commodity - without qualitative distinctions. This view does not recognize the many different attributes and services that different generation technologies can provide. The Smart Grid platform will provide the technological conditions for even more service differentiation. In this paper, we discuss elements of an alternative view for organizing the markets in a Smart Grid scenario. The key element is the consideration of electricity as a multi-attribute product. The specification of these attributes will first require a clear understanding of the Social Planner's Problem (SPP), as defined in standard economics. Such understanding will facilitate the valuation of the resource attributes with respect to the fulfillment of societal objectives. The emphasis of the paper is investigating the SPP for resource allocation, getting resources appropriately sized in advance. The solution of this problem offers insight, and provides sensible guidelines for how many resources, and of which type, are required. Our viewpoints are illustrated via a detailed investigation of the system-wide value of operational flexibility, along with the market implications. A general discussion about the role of contracts to support multi-attribute products is also included.
Sérgio Ramos, João Duarte, João Soares, Zita Vale · 5 authors
The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer's behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.
Load forecasting is highly important for power system operation and planning. Demand response, as a valuable feature in smart grid, is growing dramatically as an effective demand management method. However, traditional load forecasting tools have limitations to reflect demand response customer behaviors into load predictions. The energy consumption by demand response customers is mostly guided by their signed contracts. Therefore, existing demand response contracts are reviewed in this study for both wholesale and retail markets. An illustrative example is provided to explore the impact of these contracts on load forecasting. A concept of proactive load forecasting considering contract types is then proposed and discussed for forecasting loads in a smart grid environment.
This paper focuses on efforts to build what policymakers call the “smart grid,” involving 1) improved remote monitoring and automatic and remote control of facilities in high-voltage electricity transmission networks; 2) improved remote monitoring, two-way communications, and automatic and remote control of local distribution networks; and 3) installation of “smart” metering and associated communications capabilities on customer premises so that customers can receive real-time price information and/or take advantage of opportunities to contract with their retail supplier to manage the consumer's electricity demands remotely in response to wholesale prices and network congestion. I examine the opportunities, challenges, and uncertainties associated with investments in “smart grid” technologies. I discuss some basic electricity supply and demand, pricing, and physical network attributes that are critical for understanding the opportunities and challenges associated with expanding deployment of smart grid technologies. Then I cover issues associated with the deployment of these technologies at the high voltage transmission, local distribution, and end-use metering levels.
This paper deals with a decentralized control of smart grid by using overlapping information for load frequency of power networks introducing distributed power generations. The control objective is to minimize the cost function of load frequency control problem. We expand the state space of the system of power networks and propose a decentralized state feedback control of subsystems for the expanded system. Then we contract the decentralized feedback law to match the original system. Finally, we show the effectiveness of the load frequency control by using the proposed decentralized control method through the simulation result of decentralized large scale power network systems.
Chenye Wu, Hamed Mohsenian‐Rad, Jianwei Huang, Juri Jatskevich
With the increasing popularity of plug-in electric vehicles (PEVs), they will be able to help the power grid by providing various ancillary services. In fact, recent studies have suggested that PEVs can participate in frequency regulation. In this paper, we consider offering both, i.e., combined, frequency and voltage regulation by PEVs. In this regard, we first investigate a set of constraints that need to be taken into account on PEVs' active and reactive power flow to offer ancillary services. Next, we formulate two joint optimization problems, based on different pricing and contract scenarios, that can be solved for optimal combined offering of frequency and voltage regulation by PEVs. They address both day-ahead command-based and day-ahead price-based models. Simulation results show that the proposed designs can benefit both users and utilities.
Christine Brandstätt, Gert Brunekreeft, Nele Friedrichsen
Smart contracts based on voluntary participation and optionality can be a low transaction cost solution to implement locational signals in distribution networks and thereby avoid network investment. This paper examines the efficiency properties of smart contracts. Based on a three-node example network we show that cases exist in which smart contracts can achieve a pareto-improvement compared to the status-quo even with voluntary participation. With the pareto improvement at least one party is better of under a smart contract without worsening the situation for anyone else. We note that this requirement is very restrictive and leaves significant potential for efficiency improvements by smart contracts untapped. We then discuss the implementation of smart contracts with incentive regulation. There are two main tasks for the regulator: allowing network operators flexibility to offer such contracts and incentivizing network operators to do so.
Alessandro Di Giorgio, Laura Pimpinella, Alessandra Quaresima, Simone Curti
This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as a binary linear programming problem, the output of which specifies the best time to run of Smart Household Appliances, under a Virtual Power Threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here optimization of economic saving in case of multi-tariff contract and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manifacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.
The availability of abundant renewable resources, lack of fossil fuels and difficult geographical terrain for grid line extensions contribute to the advantages of renewable based decentralized rural electrification in Ne-pal. Solar home system (SHS) and micro-hydro are the most commonly adopted off-grid renewable energy technologies in the country. This dis-sertation examines the market of renewable energy based rural electrifi-cation within prevailing policy and programmes framework. The study verifies whether the market has been able to serve the poor in Nepal. It also captures the perception of various stakeholders (e.g. private sup-ply/installation companies, NGOs, financial institutions and the donor‘s programme) regarding the business, financing issues and the role of gov-ernment policy on the market development. In addition, the study dis-cusses and analyses renewable based rural electrification supply models, the economics behind rural electrification, market drivers and market distribution in the rural areas of Nepal. The financial mix in the off-grid rural electrification is generally charac-terized by subsidy, equity and credit. The study shows that awareness about renewable energy technologies and willingness to pay for electricity access has increased considerably. However, there is a huge financial gap between the cost of electrification and affordability among the poor. The distribution analysis shows there is significant increment in the extensive growth but decrease in the intensive growth rate of rural electrification thus indicating market expansion with uneven penetration among the ru-ral people. Solar PV technology is still not in the reach of the economic poor. Access to credit and cumbersome subsidy delivery mechanisms have been perceived as the major factors affecting the expansion of rural electrification by the stakeholders, requiring innovation in the credit and subsidy delivery system so that a larger rural population can be given ac-cess to electrification.
Andrés Molina–Markham, George Danezis, Kevin Fu, Prashant Shenoy · 5 authors
Abstract. Smart meters that track fine-grained electricity usage and implement sophisticated usage-based billing policies, e.g., based on timeof-use, are a key component of recent smart grid initiatives that aim to increase the electric grid’s efficiency. A key impediment to widespread smart meter deployment is that fine-grained usage data indirectly reveals detailed information about consumer behavior, such as when occupants are home, when they have guests or their eating and sleeping patterns. Recent research proposes cryptographic solutions that enable sophisticated billing policies without leaking information. However, prior research does not measure the performance constraints of real-world smart meters, which use cheap ultra-low-power microcontrollers to lower deployment costs. In this paper, we explore the feasibility of designing privacy-preserving smart meters using low-cost microcontrollers and provide a general methodology for estimating design costs. We show that it is feasible to produce certified meter readings for use in billing protocols relying on Zero-Knowledge Proofs with microcontrollers such as those inside currently deployed smart meters. Our prototype meter is capable of producing these readings every 10 seconds using a $3.30USD MSP430 microcontroller, while less powerful microcontrollers deployed in today’s smart meters are capable of producing readings every 28 seconds. In addition to our results, our goal is to provide smart meter designers with a general methodology for selecting an appropriate balance between platform performance, power consumption, and monetary cost that accommodates privacy-preserving billing protocols. 1