Demand Side Management Strategies Research Articles

Optimum Design of a Stand-alone Hybrid Power System with Demand-side Management

Hybridising renewable energy sources improves the reliability of renewable energy systems for stand-alone applications. However, the high system cost is one of the setbacks that contribute to the slow growth of renewable energy development. The system cost can be reduced by optimal system design. This paper presents hybrid system design strategies that minimize the system capital cost. The design concept considers integration of demand-side management (DSM) strategy in the system design process. In this strategy, the residential load is divided into three categories and prioritized two of them. This reduces the peak load, in effect reduces the system design capacity. A traditional design method was used to design a hybrid system to meet a specific daily load profile of a typical 3-bedroom residential facility in Tema, Ghana. The system capacity, system capital cost, net present cost, capacity shortage and excess generation of the system were considered as hypothesis. The algorithm of the proposed design strategy was used to design an optimal system for the same daily load profile. Upon comparison of the two systems, the following observations were made; the system capacity reduced from 8.4 kW to 4.3 kW, the initial cost and the net present cost of the optimal system reduced by 48% and 41.5% respectively, system capacity shortage was slightly improved from 9.5% to 8.8%. The costs of energy (COE) improved from 0.793 kWh to 0.742 kWh, 6.4% reduction. The results indicate that, the proposed design method reduces the system capacity by almost half which significantly reduced the system initial cost by 41.5 percent.

An Application of Soft Systems Methodology in the Evaluation of Policies and Incentive Actions to Promote Technological Innovations in the Electricity Sector

Abstract The promotion of a reliable and sustainable power system has as key drivers the development of smart grids associated with demand-side management schemes, diffusion of electric mobility, integration of larger shares of distributed (micro)generation, and the introduction of storage systems. In addition, these technological developments represent new business opportunities for several players, which should be considered by regulatory guidelines accounting for technical efficiency, economic feasibility and tariff affordability. The technical and economic characteristics of the electricity sector do not induce that the process of technological innovation happens in an endogenous manner within the sector dynamics. Therefore, public policies have a role to play in this process. This work presents an approach using Problem Structuring Methods to frame the problem of analyzing and evaluating technological innovations and associate incentive policies in the electricity sector. The results of this structuring phase using Soft Systems Methodology under different perspectives suggest a large number of issues to be taken into account that were organized as a hierarchy of objectives. These objectives correspond to the criteria of a multicriteria decision analysis model to assess policies promoting technological innovations. This methodology provides decision support to decision makers to shape policies aimed at fostering more reliable and sustainable electricity systems.

A generalised model of electrical energy demand from small household appliances

Accurate forecasting of residential energy loads is highly influenced by the use of electrical appliances, which not only affect electrical energy use but also internal heat gains, which in turn affects thermal energy use. It is therefore important to accurately understand the characteristics of appliance use and to embed this understanding into predictive models to support load forecast and building design decisions. Bottom-up techniques that account for the variability in socio-demographic characteristics of the occupants and their behaviour patterns constitute a powerful tool to this end, and are potentially able to inform the design of Demand Side Management strategies in homes.To this end, this paper presents a comparison of alternative strategies to stochastically model the temporal energy use of low-load appliances (meaning those whose annual energy share is individually small but significant when considered as a group). In particular, discrete-time Markov processes and survival analysis have been explored. Rigorous mathematical procedures, including cluster analysis, have been employed to identify a parsimonious strategy for the modelling of variations in energy demand over time of the four principle categories of small appliances: audio-visual, computing, kitchen and other small appliances. From this it is concluded that a model of the duration for which appliances survive in discrete states expressed as bins in fraction of maximum power demand performs best. This general solution may be integrated with relative ease with dynamic simulation programs, to complement existing models of relatively large load appliances for the comprehensive simulation of household appliance use.

Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids

This paper proposes a novel simulator of energy consumption patterns that allows designing demand side management (DSM) strategies without economic incentives. The simulator emulates consumers' patterns with and without installed DSM interfaces, based on both actual consumption measurements and surveys applied to the inhabitants of an existing isolated microgrid (Huatacondo, Chile) that has a particular DSM strategy without economic incentives. The simulator uses Markov chains to generate data characterizing consumption patterns without DSM and Bayesian networks for cases in which the users respond to the DSM strategy. Data obtained from the simulator are used to derive a response model of the consumers to the DSM interface, which can be included for the energy management system design. Results show that the implemented strategy can be effective and can generate savings up to 4.45% in diesel consumption for an ideal case where all the dwellings have the interface installed. Copyright © 2016 John Wiley & Sons, Ltd.

A radiofrequency heated reactor system for post-combustion carbon capture

Several problems with stabilization of electricity grid system are related to the time lag between the electricity supply and demand of the end users. Many power plants run for a limited period of time to compensate for increased electricity demand during peak hours. The amount of CO2 generated by these power installations can be substantially reduced via the development of new demand side management strategies utilizing CO2 absorption units with a short start-up time. The sorbent can be discharged using radiofrequency (RF) heating to fill the night-time valley in electricity demand helping in the stabilization of electricity grid. Herein a concept of RF heated fixed bed reactor has been demonstrated to remove CO2 from a flue gas using a CaCO3 sorbent. A very stable and reproducible operation has been observed over twenty absorption-desorption cycles. The application of RF heating significantly reduced the transition time required for temperature excursions between the absorption and desorption cycles. The effect of flow reversal during desorption on desorption time has been investigated. The desorption time was reduced by 1.5 times in the revered flow mode and the total duration of a single absorption-desorption cycle was reduced by 20%. A reactor model describing the reduced desorption time has been developed.

Integration of Demand Dynamics and Investment Decisions on Distributed Energy Resources

This research directly couples investment decisions on distributed energy resources (DERs) with demand side management (DSM) strategies that can be adopted over time by residential communities. The formulation also takes into account factors that usually contribute to long-term market variations and short-term operational volatilities. This paper uses a high resolution adaptive model (Hi-RAM) for short-term load calculations on a premise that expected dynamical effects due to DSM strategies and their interactions cannot effectively be captured by traditional forecast models. The integration of Hi-RAM into investment formulation allows for certain What-If investment scenario analysis on the use of advanced technologies, new plug-ins, and consumer response to power price fluctuations, and more. To demonstrate the significance of coupling of DER investment decisions and DSM strategies, three scenarios are presented. The base scenario (Mode I) results can be reproduced using traditional forecast models and is included for baseline analysis and benchmarking. In Mode II, new load patterns for plug-in electric vehicles are introduced. Mode III is an extended version of Mode II, where smart devices are used within households. The comparison of investment decisions from the three modes clearly demonstrate that interaction effects of DSM strategies matter.

Combined energy hub optimisation and demand side management for buildings

The deployment of innovative energy management (EM) approaches based on systematic modelling and optimisation techniques has received an increasing amount of attention in the last decade. This has been often prompted by more stringent energy policy objectives aiming at reducing carbon emissions, phasing out nuclear plants and promoting overall energy efficiency, while containing both capital and operating costs. In this respect the energy hub concept has proven to be a popular approach for operating technologies and units comprising diversified energy carriers, small-scale production units, storage devices and converter systems. Additionally, developments in communication network and control infrastructure afford the possibility, at least in principle, to actively steer and adjust the load on the demand side of the energy balance, leading to the formulation of demand side management (DSM) techniques. This paper proposes an EM solution that combines the features and advantages of both of the aforementioned approaches, i.e. the energy hub framework and DSM methods. The key idea is to combine the supply-side characteristics of energy hubs with the demand side flexibility yielded by the deployment of DSM schemes. This combined approach is validated on an existing building complex by formalizing its energy supply system as an integrated hub and by modelling its heating demand based on thermodynamic principles. Numerical results based on this experimental setup are presented, illustrating that the combined approach can lead to overall savings typically exceeding 10% compared to a baseline scenario where no EM solution is applied, i.e. where only a rule-based heuristic is employed to control the available energy assets, and underscoring the advantages brought by a systematically integrated modelling and optimisation approach. The proposed solution is thus of interest for a broad host of installations in the residential and commercial domain, and for the latter a specific real-world example has been explicitly considered and analysed. The obtained results are encouraging and warrant further analysis and investigation.

Active demand response with electric heating systems: Impact of market penetration

Active demand response (ADR) is a powerful instrument among electric demand side management strategies to influence the customers’ load shape. Assessing the real potential of ADR programmes in improving the performance of the electric power system is a complex task, due to the strict interaction between supply and demand for electricity, which requires integrated modelling tools. In this paper an analysis is performed aimed at evaluating the benefits of ADR programmes in terms of electricity consumption and operational costs, both from the final user’s and the overall system’s perspective. The demand side technologies considered are electric heating systems (i.e. heat pumps and electric resistance heaters) coupled with thermal energy storage (i.e. the thermal mass of the building envelope and the domestic hot water tank). In particular, the effect of the penetration rate of ADR programmes among consumers with electric heating systems is studied. Results clearly show that increasing the number of participating consumers increases the flexibility of the system and, therefore, reduces the overall operational costs. On the other hand, the benefit per individual participant decreases in the presence of more ADR-adherent consumers since a reduced effort from each consumer is needed. Total cost saving ranges at most between about 400€ and 200€ per participant per year for a 5% and 100% ADR penetration rate respectively.

A NOVEL APPROACH OF DEMAND SIDE MANAGEMENT FRAMEWORK FOR RESIDENTIAL CONSUMERS IN SMART GRID

In this paper, Demand Side Management scheme in a smart grid environment is proposed as a feature of reducing the peak demand for residential users and maximizing the profit for the utility. A smart grid scenario is considered for detailed analysis. Non-cooperative game theory approaches are generalized in this methodology for solving two dynamic conditions such as reducing peak demand and maximize the utility profit with respect to the users comfort. In this work a new strategy is developed among the various users for scheduling of appliances autonomously. Extensive simulations are carried out using actual load profiles and obtained results demonstrates reduction in Peak to average ratio. In addition the proposed system is analyzed by considering the changes in climatic conditions of summer and winter for a particular region.

Industrial demand side response modelling in smart grid using stochastic optimisation considering refinery process

Demand Side Management (DSM) scheme in smart grid technology provides a broader vision of the electricity consumers to participate in power management in the future era. Both residential and industrial sector are active consumers of electric power, in which industry sector are the major consumers of electric power, globally. In this proposed work, demand response modelling scheme for industrial sector is done using Resource task network (RTN) scheduling process and stochastic dynamic programming. The model is designed mathematically and for validating the results practical field data from refinery plant is used. The DR scheme proposed is a new intelligent model for industrial domain with practical approach. The scheme exhibits the refinery processing tasks for scheduling the peak loads by considering the important schedulable tasks in the unit such as distillation unit and other units which involve maximum power. Day ahead pricing scheme is considered for scheduling the loads to shift the demand from peak to non- peak periods. Different pricing schemes are also considered for comparison. The DR problem was mathematically modelled using stochastic programming through minimisation of objective function with set of industrial based constraints. The results are obtained using GAMS solver and Gurobi optimizer in Matlab. By this scheme the shifting of peak hours at different tasks level in the building establishes a reduction of 6.5% cost reduction. The DR scheme proposed is validated with practical results which exhibit to shift the demand from peak to non − peak periods, hence reducing cost.

Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming

Summary This paper proposes a cloud computing framework in smart grid environment by creating small integrated energy hub supporting real time computing for handling huge storage of data. A stochastic programming approach model is developed with cloud computing scheme for effective demand side management (DSM) in smart grid. Simulation results are obtained using GUI interface and Gurobi optimizer in Matlab in order to reduce the electricity demand by creating energy networks in a smart hub approach.

Assessing energy business cases implemented in the North Sea Region and strategy recommendations

e-harbours is a unique European project that was set out to identify viable energy business cases on the exploitation of energy flexibility, which optimise their operations to match energy demand and supply while taking account of the additional volatility in supply caused by renewable energy sources, improve energy efficiency, and reduce dependence on fossil fuels. In this paper, we propose an integrated multi-criteria decision analysis based framework to assess the relative performance of 21 energy business cases, which implemented different demand-side management strategies. Our proposed methodology has the ability to address complex problems involving multiple conflicting interests from various stakeholders, different forms of data, and different fuzzy and crisp relations. We find that business cases based on contract optimisation and offering reserve capacity were ranked relatively high, while those based on trading on the wholesale market or hybrid approaches fared less well. Despite finding viable pilot business cases, e-harbours found that there was little enthusiasm among industrial partners to scale up the pilots. Consequently, EU governments should consider offering attractive incentive programmes for industry engagement in achieving their objectives in reducing greenhouse gas emissions, improving energy supply security, diversifying energy supplies, and improving Europe’s industrial competitiveness.

Utility Oriented Demand Side Management Using Smart AC and Micro DC Grid Cooperative

DC microgrid provides a viable and more efficient option to cater for DC loads in the building space and, in particular, data centers. This paper investigates the opportunities associated for utilities to optimize industrial demand response for smart AC and DC microgrid environment thus facilitating distribution utility to reduce peak energy on the existing AC distribution system. A load shifting demand side management (DSM) technique is used to shift AC industrial loads in response to time of day (TOD) tariff. Hence, an attempt has been made to study the impact of DSM strategies with optimal shifting of AC devices in the presence of DC microgrid. Simulations are carried out on a practical distribution system having large industrial loads but it has been assumed that the AC distribution system has DC microgrid with renewables and battery storage systems (BSSs). DSM results for AC distribution grid are compared and analyzed with differing DC microgrid set ups, for example with and without battery storage. It has been observed that the DSM strategy with DC microgrid in the presence of solar renewables and battery storage can substantially reduce average energy cost for demand to response and peak load burden on AC distribution utilities.

Multi-agent based Demand Response management system for combined operation of smart microgrids

Abstract This paper presents an agent based market model for combined operation of the grid connected smart microgrids with price sensitive consumers by executing demand side management strategies. This model maintains a non-discriminative market environment among the consumers and generators of the microgrids connected across the distribution system. The novelty of this agent model is in two fold. First, a novel incentive mechanism called priority banking is proposed to encourage the consumers by giving them a share in locally available generation. This mechanism also monitors how often the consumers are utilizing the incentives and thereby updates their priority. Second, after executing the contracts obtained from the market, the loss contribution of each trade to the overall network loss is calculated using a novel network loss allocation method. The applicability and effectiveness of the proposed agent based market model are exemplified using an IEEE 37 bus distribution feeder network with two grid connected smart microgrids simulated in Open DSS and the proposed multi-agent system is built on JADE framework.

Combined residential demand side management strategies with coordination and economic analysis

In this paper combined demand side management strategy for residential consumers is studied for five households in South Africa. This study is twofold; the first part proposes an energy management system that combines demand side management strategies with a view of minimizing the consumer’s cost and reducing the power consumption from the grid. Appliance scheduling with a dedicated photovoltaic and storage system under time-of-use tariff shows that customers can realize cost savings and the power demanded from the grid is reduced by optimal scheduling of power sources. In the second part of this study, a model is developed to investigate the joint influence of price and CO2 emissions. It is found that CO2 emissions could give customers an environmental motivation to shift loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. It is also demonstrated that the consumer’s preferences on the cost sub-functions of energy, inconvenience and carbon emissions affects the consumption pattern. These results are important for both the consumer and the electricity suppliers, as they illustrate the optimal decisions considered in the presence of trade-offs between multiple objectives. A further study crucial to the consumer on economic analysis of PV and battery system showed that the consumer could recoup their initial investment within 5years of their investment.

Demand Side Management for Thermally Activated Building Systems Based on Multiple Linear Regression

The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems (TABS) based on multiple linear regression (AMLR) presented in this paper enables the application of demand side management (DSM) strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.

A Novel Integrated Approach of Energy Consumption Scheduling in Smart Grid Environment With the Penetration of Renewable Energy

In the future era, Smart grid provides phenomenal solutions for the consumers, in order for them to use the energy in more easy and efficient method. In this work, a new demand side management scheme is proposed for users with renewable sources and storage facilities. Here an optimal scheduling for home appliances is determined using stochastic dynamic programming for consumers with smart meters. Simulation results demonstrates the effectiveness of the approach with the mathematical model. Results are worked out in Generic Algebraic Modeling system (GAMS) with Gurobi optimizer as a solver which exhibits significant reduction of the electricity bills for the users with the usage of renewable energy and battery storage.

Smart energy-consumption management system considering consumers' spending goals (SEMS-CCSG)

Summary The goal of this paper is to optimize energy consumption, with minimum consumer interaction, and least impact on his/her life style, comfort and convenience. In the literature different demand side management (DSM) schemes are employed for consumer demand management. Till now, to the best of our knowledge none of researchers considered the consumers preferences, priorities, ease of use, grid stability, deviation minimization, demand curve flattening and implantation cost, collectively in a single management scheme. In this paper we proposed an innovative, cost-effective solution, to overcome most of the shortcomings of the previous solutions. In addition, it would also provide consumers' privacy as it would mask the energy usage pattern that combines psychological incentives in addition to economic benefits. The proposed Smart Energy-consumption Management System Considering Consumers' Spending Goal (SEMS-CCGS) can be used for optimized electricity consumption at any premises (residential, enterprise, commercial, etc.). The preliminary simulations showed 25.6% of electricity cost saving using SEMS-CCGS. The proposed system is appraised valuable by the utility companies whose focus is to shave peak loads. It also plays an important role in grid stability by improving the diversity factor and deviation of consumers load profile from available load curve. The simulation shows that deviation is minimized up to 45%. Copyright © 2015 John Wiley & Sons, Ltd.

IMPROVING POWER SYSTEM PERFORMANCE USING DEMAND-SIDE MANAGEMENT WITHIN SMART GRIDS ENVIRONMENT

This paper introduces a method to improve the performance of power systems using demand side management to overcome the problems of increasing load demand. Defining suitable demand reduction levels can cause a significant enhancement for the entire network. Therefore, the sensitivity factors related to each load centre are defined to rank the load centres according to their priority. According to these factors, the best required load reduction can be obtained at each load centre. The recommended reduction level has to ensure overcoming the problems associated with the voltage profile and transmission lines overloading due to the increase in load demand. The IEEE 30 bus system is used to highlight the demand-side management strategy under different loading conditions. Some simulation results are introduced to show the importance of considering the sensitivity of load centres when defining the reduction levels at each load centre. The recommended demand side management is compared with the load shedding technique as an emergency action to avoid the problems of line violation and to keep the system stable and secure. According to the obtained results, considerable improvement is achieved in the voltage profile, the network efficiency and power loss without the need for the load shedding. These results encourage the implementation of the introduced technique considering sensitivity of load centres in smart grid environment

Techno-economic optimization based approach for energy management of a stand-alone integrated renewable energy system for remote areas of India

In recent years, sustainability receives greater attention due to increasing demands and limited resources worldwide. The Indian state of Uttarakhand is rich in the availability of renewable energy resources such as solar, micro hydro, biomass, wind etc. Optimum utilization of these resources in stand-alone mode has been recognized as an economical and efficient option compared to grid extension for electrification of remotely located rural households. This paper presents a combined techno-economic optimization and energy management of a stand-alone solar-micro hydro-biomass-wind based IRES (Integrated Renewable Energy System) to meet the electrical energy demand of cluster of village hamlets of Uttarakhand state. A load shifting based DSM (demand side management) strategy has been suggested in the paper for energy management of the considered IRES. Further, optimization results without and with DSM strategy have been presented and compared. Finally, a sensitivity analysis has also been performed to evaluate the impact of different parameters on the considered system.