Incentive-based Programs Research Articles

Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources

Demand response (DR) programs and reconfiguration of distribution networks are generally adopted in the energy management (EM) problem of microgrid to enhance the technical and economical features of microgrid. Assuming a fixed configuration of distribution network, DR programs usually optimize the generation cost by encouraging the consumers to reduce their energy demands. Whereas reconfiguration of network is done for a pre-defined generation schedule and energy demand. However, separate incorporation of these two operational techniques in the EM problem may lead to a non-optimal solution. In this paper, a joint framework is proposed to integrate a novel incentive-based DR program and reconfiguration method in the EM problem of microgrid on a day-ahead time frame. The objective of the work is to minimize the fuel cost of conventional distributed generation (DG) and the cost of power purchased from the grid, while maximizing the profit for microgrid operator (MGO). The efficacy of the proposed model is tested on a static model of grid-connected 33-bus microgrid which consists of renewable energy (RE) sources and a conventional DG. To account the uncertainties in RE sources, Hong’s (2m+1) point estimation method (PEM) is considered in this work. The result confirms that the incorporation of DR program and reconfiguration method in the EM problem leads to an optimum energy schedule for the microgrid with a minimum lossy network. For the single-day operation of microgrid, it has been found that the power transfer from the grid and power lost in the network is reduced by 10.83% and 34.03% respectively.

Characterizing favored users of incentive-based traffic demand management program

Incentive-Based Traffic Demand Management (IBTDM) provides monetary incentives to encourage commuters to alter their departures spatially or temporary with the goal of alleviating congestion. With the proliferation of smartphone technology, mobility apps have become ideal platforms for carrying out IBTDM. Tremendous amounts of empirical app usage data have been collected, but research into the behavioral insights of IBTDM remains limited. It is unclear who IBTDM's target users should be, and which users are the most likely to be stable (actively use the app) and behaviorally sustainable (willing to contribute to congestion alleviation). This study aims to profile the socio-demographics of such favored users based on behavioral and socio-demographic data collected by the Metropia app. The Ensemble Empirical Mode Decomposition (EEMD) method was used for usage trend detection. The detected usage trends were then used in pattern classification to identify stable and sustainable users. Next, binary logistic regression was adopted to explore the socio-demographic characteristics of each category of users. It was found that factors including home work days, household annual income, household size and schedule flexibility played important roles in users' usage patterns and departure time decisions. Specifically, home work days and household annual income co-influenced app usage patterns. Household size and schedule flexibility were the main determinants of departure time behavior. The findings of this research can be used to guide administrators of budget-constrained IBTDM programs who need to wisely allocate their marketing budget to increase penetration among favored users as to maximize the utility of the program.

Behavior-based diabetes management: impact on care, hospitalizations, and costs

To (1) examine the impact of the Diabetes Care Rewards (DCR) program on adherence to care standards and (2) evaluate the economic impact of adherence to care standards. A retrospective observational cohort study design with propensity matching. Additional covariates adjustment was used to minimize residual imbalance. Utilization and cost data were compared between individuals enrolled vs individuals eligible for but not enrolled in the DCR program using a standard mean difference. Individuals were employees or their dependents from self-insured companies throughout the United States. Outcomes included adherence to the care standards, service utilization, and costs. A total of 3318 propensity-matched participants were included. Primary analysis revealed that enrolled members increased adherence to semiannual glycated hemoglobin, annual lipid, and annual urine albumin-creatinine ratio testing. Additionally, enrolled members experienced less utilization of high-acuity services and increased rates of physician visits. In a secondary analysis, the enrolled group was associated with greater pharmaceutical costs but lower medical costs. A behavioral science- and incentive-based diabetes management program was associated with greater rates of adherence to recommended diabetes monitoring care standards, increased routine clinic visits, decreased hospital admissions, and decreased inpatient days. Anticipated increases in pharmaceutical expenditures were offset by overall lower medical expenditures. Results indicate the economic benefits of adherence to evidence-based standards for diabetes care.

Quit and win contests in community-dwelling mental and behavioral health populations: A systematic review and pilot feasibility findings.

People with mental illnesses (MI) benefit from incentives to promote tobacco cessation. "Quit and Win" contests are community-based approaches that incentivize cessation. However, little is known about "Quit and Win" contest effectiveness among people with MI. To examine the utility of "Quit and Win" contests among people with MI. This study had two phases: (a) a systematic literature review to explore the potential effectiveness of "Quit and Win" contests for people with MI and (b) a pilot feasibility study of implementing a "Quit and Win" contest among people with MI from a community mental health program (CMHP). In phase 1, no reviewed study specifically included people with MI in their sample. Of the four cohort and five randomized controlled studies in the review, the mean reported participant quit rates at the end of "Quit and Win" contests were 76.8% and 28.3%, respectively. In phase 2, within a CMHP, four out of seven participants completed a "Quit and Win" contest, and one individual achieved cessation. People with MI may benefit from incentive-based tobacco cessation programs. Implementing a "Quit and Win" contests within a CMHP is important and requires more studies to determine feasibility and effectiveness.

Model-free demand response scheduling strategy for virtual power plant considering risk attitude of consumer

Driven by the modern advanced information and communication technologies, distributed energy resources has the great potential for energy supply within the framework of virtual power plant(VPP). Meanwhile, demand response(DR) is becoming increasingly important for enhancing the VPP operation and mitigating the risks associated with the fluctuation of renewable energy resources(RESs). In this paper, we proposed an incentive-based DR program for VPP to minimize the deviation penalty from participating in power market. Markov decision process(MDP) with unknown transition probability is constructed from the VPP's prospective to formulate the incentive-based DR program, in which the randomness of consumer behavior and RES generation are taken into consideration. Furthermore, a value function of prospect theory(PT) is developed to characterize consumer's risk attitude and describe the psychological factors. A model-free deep reinforcement learning(DRL)-based approach is proposed to deal with the randomness existing in the model and adaptively determine the optimal DR pricing strategy for VPP, without requiring any system model information. Finally, the results of cases demonstrate the effectiveness of the proposed approach.

What Drives Household Deforestation Decisions? Insights from the Ecuadorian Lowland Rainforests

Tropical forests, and more concretely, the Amazon Basin and the Chocó-Darién, are highly affected by deforestation activities. Households are the main land-use decision-makers and are key agents for forest conservation and deforestation. Understanding the determinants of deforestation at the household level is critical for conservation policies and sustainable development. We explore the drivers of household deforestation decisions, focusing on the quality of the forest resources (timber volume potential) and the institutional environment (conservation strategies, titling, and governmental grants). Both aspects are hypothesized to influence deforestation, but there is little empirical evidence. We address the following questions: (i) Does timber availability attract more deforestation? (ii) Do conservation strategies (incentive-based programs in the Central Amazon and protected areas in the Chocó-Darién) influence deforestation decisions in household located outside the areas under conservation? (iii) Does the absence of titling increase the odds of a household to deforest? (iv) Can governmental grants for poverty alleviation help in the fight against deforestation? We estimated a logit model, where the dependent variable reflects whether or not a household cleared forest within the farm. As predictors, we included the above variables and controlled by household-specific characteristics. This study was conducted in the Central Amazon and the Chocó-Darién of Ecuador, two major deforestation fronts in the country. We found that timber volume potential is associated with a higher odds of deforesting in the Central Amazon, but with a lower odds in the Chocó-Darién. Although conservation strategies can influence household decisions, the effects are context-dependent. Households near the incentive-based program (Central Amazon) have a lower odds of deforesting, whereas households near a protected area (Chocó-Darién) showed the opposite effect. Titling is also important for deforestation reduction; more attention is needed in the Chocó-Darién where numerous households are living in untitled lands. Finally, governmental grants for poverty alleviation showed the potential to generate positive environmental outcomes.

Decision-Making Tree Analysis for Industrial Load Classification in Demand Response Programs

Industrial loads play an important role in the success of demand response programs (DRPs). However, these programs may compromise the consumers' convenience, which can overshadow their real-world practicality. In response, this article provides a two-level decision-making tree approach to effectively determine the participation abilities of different industrial processes in DRPs considering various features and abilities of these customers. The level I of this framework introduces several classifying variables by which a basic criterion is extracted to classify different industrial processes applying the analytic hierarchy process (AHP). A participation factor is then introduced in level II of the suggested decision tree to estimate the participation level of different classes attained in level I. Finally, a desirability coefficient is formulated, offering the system operators an efficient indicator to verify the attractiveness of different incentive-based programs in the viewpoint of industrial customers. Implementing the presented framework on industrial customers of a region in Iran, it is shown that applying this method lends the decision-makers a hand in practically and effectively introducing DRPs for industrial customers.

A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes

The increasing pressure on agricultural production systems to achieve global food security and prevent environmental degradation necessitates a transition towards more sustainable practices. The purpose of this scoping review is to understand how the incentives offered to farmers motivate the adoption of sustainable agricultural practices and, ultimately, how and whether they result in measurable outcomes. To this end, this scoping review examines the evidence of nearly 18,000 papers on whether incentive-based programmes lead to the adoption of sustainable practices and their effect on environmental, economic and productivity outcomes. We find that independent of the incentive type, programmes linked to short-term economic benefit have a higher adoption rate than those aimed solely at providing an ecological service. In the long run, one of the strongest motivations for farmers to adopt sustainable practices is perceived benefits for either their farms, the environment or both. Beyond this, the importance of technical assistance and extension services in promoting sustainable practices emerges strongly from this scoping review. Finally, we find that policy instruments are more effective if their design considers the characteristics of the target population, and the associated trade-offs between economic, environmental and social outcomes.

PERSonalised Incentives for Supporting Tobacco cessation (PERSIST) among healthcare employees: a randomised controlled trial protocol

BackgroundSmoking is the primary preventable risk factor for disease and premature mortality. It is highly addictive and cessation attempts are often unsuccessful. Incentive-based programmes may be an effective method to...

A comprehensive linear model for demand response optimization problem

Demand Response (DR) is known as an effective solution for many power grid problems such as high operating cost as well as high peak demand. In order to achieve full potential of DR programs, DR must be implemented optimally. On this basis, determining optimal DR location, appropriate DR program and efficient penetration rate for each DR program is of great practical interest due to the fact that it guides the system operators to choose proper DR strategies. This paper presents a novel linear framework for DR optimization problem incorporated into the network-constrained unit commitment with the aim of determining optimal location, type and penetration rate of DR programs considering several practical limitations. To this end, a number of tariff-based and incentive-based DR programs have been taken into account according to the customer’s benefit function based on the price elasticity concept. The IEEE 24-bus Reliability Test System (RTS 24-bus) is used to demonstrate the applicability of the proposed model. Finally, DR optimization is analyzed with regards to different customer’s elasticity values and also different number of candidate load buses which reveal the applicability and effectiveness of the proposed methodology.

Incentive-based demand response: Case study

The present article discusses the development of a demand response system on a residential setting given that the proper management of energy resources by users can solve scarcity and energy reliability issues. The behavior of 1000 residential users is modelled through a peak control program based on economic and energy-related incentives. A demand response (DR) application is used where remuneration depends on scarcity pricing. The case study is shown including a mathematic algorithm and the application. The results obtained with the application exhibit a reduction of energy peaks, which translates into more efficient energy management which in turn is remunerated by scarcity pricing. It is concluded that the incentive-based DR program is viable both energy-wise and financially.

Mechanisms and impacts of an incentive-based conservation program with evidence from a randomized control trial.

Conservation science needs more high-quality impact evaluations, especially ones that explore mechanisms of success or failure. Randomized control trials (RCTs) provide particularly robust evidence of the effectiveness of interventions (although they have been criticized as reductionist and unable to provide insights into mechanisms), but there have been few such experiments investigating conservation at the landscape scale. We explored the impact of Watershared, an incentive-based conservation program in the Bolivian Andes, with one of the few RCTs of landscape-scale conservation in existence. There is strong interest in such incentive-based conservation approaches as some argue they can avoid negative social impacts sometimes associated with protected areas. We focused on social and environmental outcomes based on responses from a household survey in 129 communities randomly allocated to control or treatment (conducted both at the baseline in 2010 and repeated in 2015-2016). We controlled for incomplete program uptake by combining standard RCT analysis with matching methods and investigated mechanisms by exploring intermediate and ultimate outcomes according to the underlying theory of change. Previous analyses, focused on single biophysical outcomes, showed that over its first 5 years Watershared did not slow deforestation or improve water quality at the landscape scale. We found that Watershared influenced some outcomes measured using the survey, but the effects were complex, and some were unexpected. We thus demonstrated how RCTs can provide insights into the pathways of impact, as well as whether an intervention has impact. This paper, one of the first registered reports in conservation science, demonstrates how preregistration can help make complex research designs more transparent, avoid cherry picking, and reduce publication bias.

Haussmannian building repair, rehabilitation and strengthening: case study from France

Over time, Haussmannian buildings have been continuously subjected to conservation works, particularly considering their age and deterioration level. In recent years, several apartment buildings have been converted into luxury hotels and existing ones have been renovated due to an increasing demand for accommodation, resulting in heavy repair, rehabilitation and strengthening works. Haussmannian architecture is an invaluable asset of Paris urban character and distinctiveness, and conservation interventions should be carried out accordingly. This paper describes the rehabilitation and strengthening works best practices undertaken during these interventions. In this context, a reference to protection measures to ensure the preservation of buildings registered on the list of historic monuments is done, as well as a reference to regulatory-based and incentive-based programmes to prevent abandonment and lack of maintenance. This paper is based on the experience gained during construction projects that the author has worked on in France. The techniques and methodologies presented in this paper should be considered good conservation practices and included in maintenance programmes, but also to support the development of innovative and sustainable rehabilitation and strengthening techniques, aiming to preserve this important building heritage and similar ones existing in other countries.

Modified deep learning and reinforcement learning for an incentive-based demand response model

Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security.

Europeanizing Universities: Expanding and Consolidating Networks of the Erasmus Mundus Joint Master Degree Programme (2004–2017)

The Europeanization of higher education has gained considerable scope and momentum over the past quarter century. Whereas the coordinative Bologna process, with soft governance mechanisms, has facilitated standardization across countries, European Commission funding programs target universities more directly. The Erasmus Mundus Joint Degree Programme, as an incentive-based program, epitomizes the dynamics of such European funding management. Notably, it has established expanding university networks across Europe and unique new tertiary degrees that facilitate international student mobility. Applying social network analysis to 561 participating universities through several program cycles, we longitudinally examine three key patterns in the program’s development: the expansion of the program, the consolidation of pan-European networks, and the participation of and coordination by central universities in these Europeanization processes. Program participation increased considerably across cycles, even as established networks were consolidated, largely through re-accreditation of established programs. Moreover, we identify those universities that assume a central position in the inter-organizational structure of this international program. These universities actively facilitate the evolving Europeanization of higher education by strengthening inter-university networks via a signature EU program.

Integration of Optimal Time-of-Use Pricing in Stochastic Programming for Energy and Reserve Management in Smart Micro-grids

In this paper, the optimal operation problem of smart micro-grids integrated with the pricing of Time-of-Use (TOU) demand response (DR) program is modeled as a two-stage stochastic programming problem with the aim of minimizing the cost of MG operation and running TOU in the presence of renewable resources and incentive-based DR programs. Here, TOU as the most common type of time-based DR programs is implemented using a linear function based on the concept of self- and cross-price elasticity of load demand. In the presented model, the forecasting errors of generation of renewable resources are modeled by probability density functions. The operator of MG decides on two stages for optimal management of its network; the first stage refers to the operation of base condition of MG and the second one is pertaining to after the realization of different scenarios for generation of renewable resources. The base condition of MG refers to the situation in which the active power productions of renewables are equal to the predicted values. The proposed model is solved by Particle Swarm Optimization algorithm. A typical MG is employed to investigate and analyze the different features of the method. By varying the demand response potential of MG consumers, TOU tariffs are determined, and their impact on the results of energy and reserve cost as well as voltage and load profiles of MG are analyzed. Numerical results show the efficiency of DR in reducing costs as well as covering the uncertainty resulting from renewables.

Going Once, Going Twice: Using Willingness-to-Accept Auctions to Promote Smoking Cessation.

Incentive-based smoking cessation interventions increase quit rates. The optimal incentive, however, is unknown. We used a willingness-to-accept (WTA) auction where smokers submitted bids indicating the incentive they would need to receive for 1 week of smoking cessation. Smokers ready to quit (n = 35) participated in a WTA auction, naming the amount they needed to be paid to quit for 1 week. Auction winners received an incentive if they successfully quit. All smokers received nicotine replacement therapy and self-help cessation materials. Carbon monoxide concentrations were monitored remotely three times during the week and in person at the final session. Participants who smoked their first cigarette within 5 min of waking demanded a significantly larger incentive in exchange for quitting than those who waited (p < .05). About 45% of auction winners quit smoking compared to 20% of nonwinners (p < .05). The cost per quit was $466 among auction winners compared to $894 among nonwinners. WTA auctions may be an effective way to determine the amount smokers need to be paid to quit, which would allow researchers to estimate the most cost-effective payment to offer as part of incentive-based smoking cessation programs. Replication evaluating longer-term cessation outcomes with larger samples is warranted. Incentive-based smoking cessation interventions increase quit rates. However, the optimal incentive is unknown. We found that a WTA auction is a feasible technique for estimating the amount smokers need to be paid to quit. Incentives are a surprisingly cost-effective way to facilitate smoking cessation because they are only paid to those who successfully quit. WTA auctions are a promising tool for determining the incentive that strikes the optimal balance between being large enough to motivate cessation, but small enough that it can be offered to the largest possible population of smokers for a given program budget.

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

Economic, pro-social and pro-environmental factors influencing participation in an incentive-based conservation program in Bolivia

• Economic, pro-social and pro-environmental factors can influence household participation in forest conservation programs. • Owning property titles, having large forested land, agricultural equipment and off-farm incomes favour participation. • Social networks, compliance with social norms of reciprocity, and a lack of trust in public institutions may increase participation. • Awareness of environmental problems and perception of the gains associated with conservation may increase participation. • Feeling some individual responsibility for environmental issues may increase participation in more restrictive agreements. The effectiveness of incentive-based conservation programs depends on how they influence and interact with multiple motivations of the participants. Here, we studied an incentive-based program for forest conservation in Bolivia – called “Reciprocal Watershared Agreements” – that mixes material compensations with pro-social and pro-environmental motivations as a way to reduce crowding-out of intrinsic motivations and to increase participation. Based on a sample of 470 households who were offered the program, we studied household characteristics that influenced (i) the probability of participation in the program, (ii) the intensity of the participation, measured as the area allocated in the agreement, and (iii) the modality of participation, measured as the probability of participation in the different types of agreements. We found that owning property titles, having large forested land with low conservation opportunity cost, agricultural equipment and off-farm incomes seem to favour participation. In addition, the probability of participation increases with some pro-social factors, such as a deeper or older integration into social networks, and greater compliance to social norms of reciprocity. We also found that a lack of trust in public institutions can increase both the likelihood and the intensity of participation, as can certain pro-environmental factors, such as awareness of environmental problems, knowledge about potential solutions to solve them and perception of the gains associated with the conservation of ecosystem services. Finally, we found that feeling some individual responsibility for environmental issues and some difficulty in performing pro-environmental options may increase participation into more restrictive agreements. Our results thus highlight the factors that could increase uptake and factors on which programs might focus in order to have a greater impact on pro-environmental behaviours. They also suggest that incentive-based program can be designed to take advantage of pro-social and pro-environmental motivations as strongly as of economic ones.

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.