Demand Side Management Measures Research Articles

Challenges and Opportunities in Electric Vehicle Charging: Harnessing Solar Photovoltaic Surpluses for Demand-Side Management

The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.

Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

Reassessing the need for carbon dioxide removal: moral implications of alternative climate target pathways

Abstract Non-technical summary Scenarios compatible with the Paris agreement's temperature goal of 1.5 °C involve carbon dioxide removal measures – measures that actively remove CO2 from the atmosphere – on a massive scale. Such large-scale implementations raise significant ethical problems. Van Vuuren et al. (2018), as well as the current IPCC scenarios, show that reduction in energy and or food demand could reduce the need for such activities. There is some reluctance to discuss such societal changes. However, we argue that policy measures enabling societal changes are not necessarily ethically problematic. Therefore, they should be discussed alongside techno-optimistic approaches in any kind of discussions about how to respond to climate change. Technical summary The 1.5 °C goal has given impetus to carbon dioxide removal (CDR) measures, such as bioenergy combined with carbon capture and storage, or afforestation. However, land-based CDR options compete with food production and biodiversity protection. Van Vuuren et al. (2018) looked at alternative pathways including lifestyle changes, low-population projections, or non-CO2 greenhouse gas mitigation, to reach the 1.5 °C temperature objective. Underlined by the recently published IPCC AR6 WGIII report, they show that demand-side management measures are likely to reduce the need for CDR. Yet, policy measures entailed in these scenarios could be associated with ethical problems themselves. In this paper, we therefore investigate ethical implications of four alternative pathways as proposed by Van Vuuren et al. (2018). We find that emission reduction options such as lifestyle changes and reducing population, which are typically perceived as ethically problematic, might be less so on further inspection. In contrast, options associated with less societal transformation and more techno-optimistic approaches turn out to be in need of further scrutiny. The vast majority of emission reduction options considered are not intrinsically ethically problematic; rather everything rests on the precise implementation. Explicitly addressing ethical considerations when developing, advancing, and using integrated assessment scenarios could reignite debates about previously overlooked topics and thereby support necessary societal discourse. Social media summary Policy measures enabling societal changes are not necessarily as ethically problematic as commonly presumed and reduce the need for large-scale CDR.

Application of a flexibility estimation method for domestic heat pumps with reduced system information and data

Activation of heat pump flexibilities is a viable solution to support balancing the grid via Demand Side Management measures and fulfill the need for flexibility options. Aggregators as interface between prosumers, distribution system operators and balance responsible parties face the challenge due to data privacy and technical restrictions to transform prosumer information into aggregated available flexibility to enable trading thereof. Thereby, literature lacks a generic, applicable and widely accepted flexibility estimation method for heat pumps, which incorporates reduced sensor and system information, system- and demand-dependent behaviour. In this paper, we adapt and extend a method from literature, by incorporating domain knowledge to overcome reduced sensor and system information. We apply data of five real-world heat pump systems, distinguish operation modes, estimate power and energy flexibility of each single heat pump system, proof transferability of the method, and aggregate the flexibilities available to showcase a small HP pool as a proof of concept.

Smart meter-related data privacy concerns and dynamic electricity tariffs: Evidence from a stated choice experiment

Data privacy concerns are frequently raised to explain households’ reluctance to have smart meters. These meters are a prerequisite for future energy systems as they enable demand-side management measures, such as dynamic electricity tariffs. Consequently, two questions arise: First, what are the determinants of smart meter-related data privacy concerns? Second, how do these concerns affect households’ preferences concerning dynamic electricity tariffs? Based on a representative household survey conducted in Germany, I find linear and negative relationships between privacy concerns and respondents’ general level of trust and technological affinity. Interestingly, high levels of energy-related financial literacy and environmental values are associated with a greater probability of expressing either low or high privacy concerns. Using a choice experiment, I subsequently identify smart meter-related data privacy concerns as a mediating factor in households’ aversion to dynamic pricing. Furthermore, I find heterogeneous effects of additional information regarding the handling of consumption data on respondents’ aversion to dynamic electricity tariffs. Additional information primarily reduced the aversion of respondents with high data privacy concerns. This finding has potentially far-reaching implications for other products and services that utilize information about individual behavior.

Urban water security: water supply and demand management strategies in the face of climate change

ABSTRACT Understanding the knowledge of climate-change impacts on water-resources is a priority. This article goes a step further with the main objective of this study to explore water-practitioners’ viewpoints regarding the water supply-side and water demand-side management measures in coping with future climatic impacts to achieve urban water security. Interviews were conducted with water-professionals from regional urban water authorities of Queensland, Australia. From a water-practitioner’s viewpoint, surface water is perceived to remain a high-priority water resource for the region, although climate-change is projected to make this resource more vulnerable in the area. Climate-change risks have rarely been considered as a selection-criterion when long-term water solutions were investigated by water-practitioners. Although non-pricing water demand management policies were used to reduce water demand in the region, pricing-based water demand management tools are perceived by the water-professionals to be the most effective at reducing water demand in the study area, if implemented.

Assessment of low-carbon energy transitions policies for the energy demand sector of Cameroon

This study assesses Cameroon's future energy demand, associated greenhouse gas (GHG) emissions and the impact of various low-carbon transition policies on the energy system from 2016 to 2045. The Low Emissions Analysis Platform (LEAP) model was used to develop the Reference (REF scenario) and three alternative scenarios: the Medium Transition Scenario (MTS), High Transition Scenario (HTS) and Advanced Transition Scenario (ATS). The alternative scenarios define varying ambitions in implementing energy efficiency, demand side management and fuel switching policies on the energy demand sector in the country for socioeconomic development. Results for the REF scenario shows the energy demand and associated GHG emissions increase to 9079.98 ktoe and 10.2 Mt CO2e by 2045, representing annual growth rates of 3.53 % and 2.69 % respectively. The alternative scenarios all experience varying and lower growth rates compared to the REF scenario, the least under the ATS. This indicates switching to cleaner fuels in the residential and transport sectors hold the highest potential for emission savings. The study further recommends key energy efficiency and demand side management measures, financial incentives and other strategies the government could adopt to achieves significant energy emissions reduction.

Time of Use tariffs, childcare and everyday temporalities in the US and China: Evidence from time-use and sequence-network analysis

Time of Use (ToU) tariffs that aim to shift people’s energy use away from peaks have been key demand-side management measures. Yet, their effectiveness has often been below expectations. In this paper we empirically test arguments based on social practice theory that this underperformance is not the result of people making ill-informed or economically questionable decisions but reflects the complexities of social synchronization. Using sequence-network analysis, we visualize and comparatively analyze time-use data from the US and China to capture everyday temporalities of a particular time-inflexible group – child caregivers. Findings show that timing and sequencing of peak activities for caregivers in both countries were largely structured by institutional and family rhythms, though with considerable differences in extent and timing of influences due to diverging childcare cultures. The necessity to follow these rhythms leaves caregivers little room to adjust their peak activities to ToU tariffs, turning this well-intended measure into an inequitable financial burden on the group. We argue that policymakers should deploy time-varying tariffs in ways that align with everyday temporalities of population groups. Cutting critical and daily peaks in ways that work with people’s temporal (in)flexibilities in everyday practices could be a key to the effectiveness of energy measures.

Improving the building energy flexibility using PCM-enhanced envelopes

Pre-formed internal insulative panels with impregnated phase change materials (PCM) can significantly increase both the thermal resistance and thermal capacitance of existing or new building envelopes, thereby improving the overall energy performance of buildings. A further advantage is that such measures have the potential to enhance the energy flexibility of the building, thereby offering the possibility of participation in demand side management measures such as demand response programmes. The current literature on building envelope physics lacks research on energy flexibility and demand response, especially in the context of the building envelope integrated design with high latent heat materials such as PCM for demand response applications. The objective of the current study is to examine how the addition of PCM impregnated building envelopes affects both the thermal performance of the building envelope, as well as the wider building energy characteristics when subject to different demand response events. A reference building is utilised, which is a residential detached house with a floor area of 160 m 2 and a south-easterly facing aspect. Another contribution of this study is proposing new energy flexibility indicators taking into consideration envelope pre-cooling and pre-heating periods prior to the demand response event. Simulation results show that shorter envelope pre-cooling periods (0.5 hr) together with longer demand response periods (4 h) are preferable for all envelopes to achieve the maximum power curtailment for cooling. PCM-enhanced envelopes are shown to give best cooling demand shifting and energy flexibility efficiency. The MW PCM-1 and MW PCM-2 envelopes have the highest flexibility efficiency with a value of 244%. For heating, gypsum board enhanced with PCM retrofitted on the envelopes are shown to give an overall good performance in energy flexibility efficiency and in power curtailment compared to the other building envelopes in all durations of an energy flexibility event. For heating, the maximum energy flexibility efficiencies range from 250% for the LW Gypsum Board envelope to 356% for the LW PCM-2 envelope. • Energy flexibility is mapped for building envelopes with sensible and latent TES. • Building envelopes are optimised for different demand response events. • An energy flexibility sensitivity analysis algorithm is developed in EnergyPlus. • In buildings with only sensible TES short demand response events should be used. • Envelopes with PCM have better energy flexibility in long demand response events.

Optimal design and energy management of residential prosumer community with photovoltaic power generation and storage for electric vehicles

The optimum design and management of residential photovoltaic (PV) communities are essential to the reliability to satisfy the demands of communities due to the volatility and intermittence of solar energy. The retailer for energy dispatch management is introduced to improve the economic performances of PV communities through demand-side management measures with the electric grid. This paper proposes a novel two-stage optimization model of design and electricity dispatch strategies for residential PV communities to operate electric vehicles' charges. The first-stage optimization aims to obtain the optimal capacities of PV and batteries. The second-stage optimization optimizes the retailer's hourly electricity prices and electricity dispatch strategies, in which the Stackelberg game is performed between maximizing the retailer's benefit and minimizing the electricity cost of residential basic loads and vehicle charging loads. A case study is implemented to validate the proposed model. The installation capacity of batteries is appropriately increased with the increasing active loads of electric vehicles. The optimal pricing strategies of the retailer based on the trade schemes with the electricity grid are influenced by their ratios of fixed and active loads. Compared to the community without the retailer, the annual electricity cost of users is saved by 21.2 % through the proposed management strategy.

An analysis of the interactions between daily electricity demand levels in France

This research proposes a new system that classifies electricity demand in both levels and time periods and uses intraday data to provide a deeper understanding and inform the design of appropriate demand-side management measures. This classification divides the day into three periods with contrasting electricity demand levels; two with peaks, 6:15 a.m. to 5 p.m. and 5:15 p.m. to 11:45 p.m.; and one with a substantial drop in demand from 0 a.m. to 6 a.m.. The interactions of daily electricity demand levels in France from 2013 to 2021 were empirically analysed and revealed a need to implement demand-side management policies to shift demand from peak to valley periods.

Market fragmentation of energy resource prices and green total factor energy efficiency in China

This paper studies the market fragmentation condition of energy resource (such as coal, gasoline, diesel and electricity) prices by using the relative price method and analyzes its impact on China's green total factor energy efficiency. Results shows that: (1) Market fragmentation of China's energy resource prices presents an inverted U-shaped curve in 2004–2009, and then tend to be stable in 2010–2018 with a peak in 2007. The market fragmentation of China's energy resource prices of energy transfers out provinces is more serious than that of energy transfer in provinces. (2) The trend of China's green total factor energy efficiency presents N-shape curve in 2004–2014, and then it tends to be stable around 0.850 in 2015–2018. The green total factor energy efficiency of energy transfers out provinces is lower. Phenomenon of “energy efficiency curse” is significant in China. (3) Impact of market fragmentation of energy resource prices on green total factor energy efficiency also presents an inverted U-shaped curve. And at current stage, reducing the degree of market fragmentation of China's energy resource prices is conducive to the improvement of green total factor energy efficiency. According to the study results, we believe that reduce the market fragmentation of energy resource prices and promote the internal opening of the energy market, promote demand side management measures such as energy performance contracting, reduce the proportion of coal consumption in total energy consumption and improving energy efficiency, increase green total factor energy efficiency of energy transfer out provinces are all effective ways to improve China's green total factor energy efficiency.

Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers

A microgrid comprises renewable and non-renewable power generating sources, controllable loads, energy storage devices and works as a single controllable entity. A gradual shift from conventional internal combustion engine-based vehicles to electric/hybrid electric vehicles has also led to a new load type in the power system. Moreover, demand-side management measures like demand response programs have become popular. This work deals with the optimal coordinated operation of a grid-connected AC microgrid consisting of controllable and uncontrollable power sources, battery storage units, considering plug-in hybrid electric vehicles and demand response programs. Stochastic models of renewable power sources, electric load demand, loads of hybrid electric vehicles (with battery charging characteristic), and grid power price are fed into “Hong’s 2 m point estimate method” embedded optimal operating strategy. The objective is to minimize the cost of operation subject to the satisfaction of technical constraints. A nested stochastic optimization algorithm is implemented to find optimal generation schedule, battery dispatch strategy, and the best incentive value for an incentive-based demand response program. Different charging strategies of hybrid electric vehicles are studied, and their impacts on system operation are investigated. The optimal coordination between a voltage control scheme using a smart transformer with the energy management scheme is also investigated. Simulation studies on a thirty-three bus test system prove the efficacy of the proposed algorithm. The proposed coordinated optimal operating strategy reduces the operating cost by 17.53%∼17.74%. The system loss also reduces by 29.49%∼31.36%.

Optimal planning of hybrid renewable energy system considering virtual energy storage of desalination plant based on mixed-integer NSGA-III

Due to the shortage of freshwater and energy crisis, seawater desalination with renewable energy has been proposed. A method for determining optimal planning of coastal hybrid renewable energy system (HRES) is proposed in this paper. Firstly, the system composition is introduced, and the virtual energy storage (VES) characteristics of the seawater reverse osmosis desalination plant is utilized to accommodate renewable energy generation. In order to reduce the system total cost, demand side management (DSM) measures are designed and applied to the regular electric load in the system. The methodology for planning optimization of the system is suggested, with objectives aiming at minimizing system total cost, end-user satisfaction loss caused by DSM, and tie-line power fluctuation. Since the problem has a mixed-integer manner, a modification of Non-dominated Sorting Genetic Algorithm-III (NSGA-III) is proposed. The newly-developed NSGA-III variant, named as MINSGA-III, is capable of solving mixed-integer optimization problem in an evolutionary algorithm fashion. This paper also presents several case studies to verify the feasibility and merits of the proposed method. The simulation results show that the proposed method is effective and feasible, and both DSM measures and VES approach have positive impact on the system modelling. The proposed MINSGA-III can effectively determine the solution of the multi-objective optimization problem.

Techno-Economic Modelling of Micro-Hydropower Mini-Grids in Nepal to Improve Financial Sustainability and Enable Electric Cooking

In rural Nepal, micro-hydropower plant mini-grids provide renewable electricity to thousands of communities but the plants often have poor financial sustainability. Widespread uptake of electric cooking in such communities is currently not feasible due to high peak loads and limited capacity. In this paper, we develop a Remote-Areas Multi-Energy Systems Load Profiles (RAMP)-based stochastic techno-economic model for evaluating the economic viability of off-grid communities and improving their financial sustainability by introducing new appliances, productive end uses, and demand-side management measures. The model can be used to understand community electricity demand, assess economic status, determine equitable and profitable tariff structures, and plan new connections including electric cooking promotion or new industrial machines. Detailed electric cooking load modelling functionality was developed to represent Nepali cooking practices, scalable to approximate widespread uptake of electric cooking, and adaptable to other cookers and contexts. The model showed that a payment structure based on electricity consumption rather than a flat tariff could increase the income of a case study community in Eastern Nepal by 400%, although increased monthly payments for certain households from NPR 110 (USD 0.93) to NPR 500–1100 (USD 4.22–9.29) could present difficulty. However, households could reduce their electricity consumption and a more equitable tariff structure could be chosen while preserving plant profitability. The number of industrial machines such as mills could be doubled and up to 40 households provided with electric cookers if demand-side management measures were introduced.

Optimal energy management of smart building for peak shaving considering multi-energy flexibility measures

Multi-energy flexibility measures comprising energy substitution and demand-side management (DSM) can enhance the control of buildings and help them participate in the energy market, obtaining greater profit margins. However, the application of these flexibility measures is also subject to many limitations. For example, DSM is a kind of load redistribution process in which the energy payback constraints associated with comfort or usage demand should be considered. Therefore, this work studies the optimal energy management of a building energy system (BES) considering multi-energy flexibility measures, specifically under the energy payback effect, to better guide the peak shaving strategies. First, energy substitution measures are proposed involving energy conversion and storage modeling. Second, a novel dynamic two-step DSM measure is modeled for the reduction and recovery process. Then, a mixed-integer and linear programming (MILP)-based energy management model is developed to optimize the operation of smart buildings for peak shaving. The case studies demonstrate that 1) a BES can obtain better profit by utilizing multi-energy flexibility measures; 2) optimized multi-load recovery strategies can enhance the flexibility potential of a BES; and 3) a reasonable multi-load recovery mechanism should be established to offset the energy payback effect.

Energy management for the industrial sector in smart grid system

Industrial energy management is intended for the most significant specific indicator of the Energy Framework. Although it is much involved in growing energy generation and distribution due to demand-side management is increasing awareness in research. In a deregulated power marketing system, that is the dynamic critical condition between utilities and industrial consumers for energy supply and management. The smart grid topology is maintaining energy management ability to provide facilities to be competitive internationally. Energy demand-side management measures to maintain the energy framework in terms of energy consumption. In this paper, the energy demand response project has been used to manage loads efficiently by utilizing the smart grid for industrial settings. This article also provides a demonstration and numerical analysis of individual demand for industrial consumers about indications of the financial load model for energy demand. This program provides an attractive incentive for the industrial consumers due to reducing the loads on utilities and intelligently manages during energy crisis periods, resulting in the highest peak load that is running in off-peak time. In this paper, the mathematical and graphical outcomes are analyzed for industrial energy management.

Quantifying the Demand Response Potential of Inherent Energy Storages in Production Systems

The increasing share of volatile, renewable energy sources rises the demand for consumers who can shift their electrical power demand in time. In theory, the industrial sector offers great potential here, as it accounts for a large proportion of electricity demand. However, the heterogeneous structure of facilities in factories and the concerns of operators regarding data security and process control often prevent the implementation of demand side management measures in this sector. In order to counteract these obstacles, this paper presents a general mathematical framework for modelling and evaluating different types of inherent energy storages (IES) which typically can be found in industrial production systems. The method can be used to calculate the flexibility potential of the IES in a factory with focus on hysteresis-controlled devices and make the potential visible and usable for power grid stabilization. The method is applied in a typical production line from metalworking industry to provide live monitoring of the current flexibility potential of selected devices.

State-of-the-Art Integration of Decentralized Energy Management Systems into the German Smart Meter Gateway Infrastructure

The German Smart Meter Gateway (SMGW) infrastructure enables digital access to metering data and distributed energy resources by external parties. There are, however, various restrictions in order to guarantee the privacy of consumers, and strong security requirements. Furthermore, in the current state of development, there are still several challenges to overcome in order to implement demand side management (DSM) measures. In this paper, we present a prototype enabling DSM measures within the SMGW infrastructure, using the smart grid traffic light concept. The prototype implements an automated decentralized energy management system (EMS) that optimally controls an electric vehicle charging station. In the development of this prototype, we did not only evaluate five of the seven available SMGW devices, but also push the limits of the infrastructure itself. The experiments demonstrated the successful implementation of the intended DSM measure by the EMS. Even though there are technical guidelines standardizing the functionality of SMGWs, our evaluation shows that there are substantial differences between the individual SMGW devices.

Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management

Enabling P2P energy trading among prosumers (both producers and consumers) is a promising paradigm in the decentralized energy era. It is important to design a fair pricing strategy for energy trading; however, it leads to a complicated problem particularly when multi-energy systems participating the energy trading. This study proposes a trading aiding tool, which is based on a Nash-type non-cooperative game model between residential and commercial prosumers with guaranteed trading fairness. The model is generalized considering commonly used energy supply technologies and various demand-side management measures. The energy cost for both residential and commercial prosumers can be minimized with fair pricing strategies for both electricity and heating trading. Compared to previous research, this study presents a more concise solution to determine the fair prices for multi-energy trading. Through the McCormick relaxation, the complex problem is linearized as a Mixed Integer Linear Programming (MILP) model with significant improvement on computational efficiency. A case study is conducted in Shanghai, China, where a community is modeled as the residential prosumer and connected to the commercial prosumer including three commercial buildings. The results indicate that compared to two prosumers standing alone, enabling energy trading with fairness can achieve 4.9% cost saving; the fair-trading prices for electricity and heating are 0.090 $/kWhe and 0.015 $/kWhh, respectively. Overall, this study proposes an efficient tool to provide insights into optimal infrastructure designs, and further quantify the fair pricing and schedule of demand response during P2P energy trading.