Research on user optimal aggregation based on demand response potential spectrum clustering analysis

To reduce the cost of load aggregators (LA), recent studies suggest optimizing LA’s bidding plans by utilizing the demand response (DR) potential of various flexible end consumers. With the exploding growth of IT demand, an emerging flexible end consumer, data centers, has begun to appear around the world. In this paper, we focus on utilizing the data centers’ DR potential to optimize the bidding plans of data center aggregators (DCA), which is the LA that supply power for multiple data centers. First, an optimization bidding model is formulated based on data centers’ DR potential, which consider the backup energy characteristics and workloads spatiotemporal transfer characteristics of data centers. Then, in view of the unique load characteristics of data centers, we propose a rational and quantitative compensation mechanism for the DCA to induce data centers participating in DR. In the end, simulation results are used to prove that the proposed model is beneficial to both the DCA and data centers.

As an intermediary between residential customers and system operators, load aggregators (LA) are responsible for integrating the residential customers' demand response (DR) potential and enabling their transactions in the day-ahead market. Profit of the market trading process mainly relies on the pre-estimated achievable DR potential, which puts forwards the necessity of its accurate forecasting. Therefore, this paper proposes a probabilistic forecasting model to forecast the day-ahead achievable DR potential at the aggregated level under an incentive-based demand response (IBDR) program. Firstly, we attempt to establish the customers' DR potential, during which a home energy management system (HEMS) is introduced to implement load adjustment for electrical appliances. Secondly, several features that may affect the DR potential are extracted, among which the more relevant ones are selected through the support vector machine recursive feature elimination (SVM-RFE) method. Finally, based on these selected features, a support vector machine (SVM) method is adopted to establish the DR potential point forecasting model, and then the probabilistic forecasting model of the aggregated DR potential is established through the superimposition of the point forecasting results and the corresponding error distribution, the latter could be estimated by the non-parametric kernel density (NKDE) method. Case studies show that a good performance could be achieved by the proposed probabilistic forecasting model and the feature selection process could significantly improve the forecasting accuracy.

Trend and determinants of tuberculosis treatment outcome in a tertiary hospital in Southeast Nigeria

This study investigates Asia-Pacific corporate bond rating transitions, defaults, and defaulted bond recoveries. As the size of the market continues to grow and mature, the speculative-grade part of the bond market has become a significant portion of the total bond market in the Asia-Pacific region. Despite this growth, Asia-Pacific issuers historically have had higher average ratings and lower default rates than the global sample. However, the speculative-grade Asia-Pacific issuers - excluding Japan - have higher default rates than their global counterparts. Most of the bond defaults happened during the Asian financial crisis of 1998-1999. Since the financial crisis the default rates have declined as companies have been able to repair their balance sheets. One exception to this pattern of higher default rates in the region is Japan, where there has been only one Moody's-rated default since 1990. Japan's relatively limited rated-bond default experience is attributable to its higher credit quality (bond market access has historically been very limited for speculative-grade issuers) and the general practice of its government and financial institutions to support companies facing financial distress. The recovery rates and annual credit losses for defaulted bonds in the region are comparable to those in Europe and North America.

Accurate forecasting of demand response (DR) potential is of great significance for load aggregators (LAs)to bid in the DR market and reduce their market trading risks. Current DR potential forecasting methods usually ignore the spatial correlation between different types of customers, resulting in large errors when the DR potential varies dramatically. To this end, this paper proposes a day-ahead DR potential forecasting model for LAs based on a graph convolutional network (GCN), considering the spatial-temporal correlation between different types of customers. Firstly, customers are divided into different clusters using the K-means algorithm. Secondly, the multiple influencing features of each customer cluster are extracted and the spatial-temporal correlation matrices are established by Pearson correlation coefficients (PCCs). Finally, this graph structure with spatial-temporal correlation information is used to train the GCN forecasting model. Case studies verified the validity of the proposed DR potential forecasting model.

Facing to the peak regulation market, a load aggregator should bid price and capacity to participant in market competition. Considering different loads demand response abilities, the selective combinatorial game mathematical model is built in this paper, so as to acquire optimal bidding schedule. The objective function minimizes demand response costs of the load aggregator. Meanwhile, constraints include peak regulation capacity, demand response potential, power usage demand, and so on. Load user in the load aggregator would provide its own reduction capacity via demand response function, and consider other loads' demand response through learning factors. The load aggregator would select optimal load combination to bid optimal price and capacity for peak regulation market. Simulation cases numerically prove the effectiveness of the selective combinatorial game model presented in this paper.

As more load devices are connected to the power grid, the refined management and control requirements of the distribution network cannot be satisfied by the traditional extensive load aggregation, leading to the development of response potential being limited. In this regard, a hierarchical aggregation model considering translatable loads, temperature-controlled loads and user response willingness is proposed, and an analysis model for aggregation scheduling potential based on user response willingness is established in this paper. The devices with similar characteristics are clustered and aggregated, while a user response willingness model considering user background information is established by analyzing and processing of load characteristics. On this basis, the transfer situation of the aggregated power of the translatable load under the time-of-use price and the aggregated scheduling potential of the temperature-controlled load under the temperature control are explored. The analysis results show the proposed method can improve the aggregation accuracy, simulate the load transfer situation effectively, and tap the potential of load aggregation scheduling, which has guiding significance for the fine aggregation and regulation of load.

As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.

Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

Demand response can involve demand side resources instead of supply side resources into electricity market services. Firstly, this paper establishes a demand response structure based on load aggregator, which can aggregate the response resources of small and medium-sized users to participate in the demand response. Secondly, the response uncertainty of the main flexible load on the user side is analyzed, and the model of user's response difference is established. Then, this paper proposes an LA's response strategy that combines energy storage equipment to eliminate the risk of LA's insufficient response caused by user's response uncertainty, and the contact quantity of energy storage equipment and the optimal economic incentives can be determined through the LA's response profit maximization function. Finally, the effectiveness of the proposed model is verified by a simulation.

Under the background of the deepening reform of power market, load aggregators can aggregate adjustable flexible load resources to participate in the market, which can effectively alleviate the problem of power supply resource shortage. However, how to select the aggregation object and how to design the demand response package are urgent problems for load aggregators. This paper first evaluates the potential of load demand response and conducts user clustering based on its potential. Combined with the theory of cloud model, the evaluation level of user demand response ability is constructed, and the aggregated user object is further selected. Secondly, considering that the load aggregator supplies power to the user, the power can be purchased from the grid or generated by itself, the load aggregator - user two-layer model is established considering the adjustable characteristics of the load. Then through KKT condition analysis, the two-layer model is transformed into a single-layer model, and Gurobi solver is invoked to optimize the solution. Finally, an example shows that the model can optimize the demand response packages for load aggregators and soften the user side load demands.

In the process of demand response execution, the incentive intensity directly affects the demand response potential of energy-intensive enterprise. Taking the silicon carbide enterprise as an example, firstly, the production process of silicon carbide and the adjustment characteristic of key equipment smelting furnace is analyzed. Then, a refined cost model that considers various costs and benefits of demand response is established, and the optimization goal is to minimize the operating cost of the enterprise. Finally, the influence of different incentive subsidy on the user's demand response potential is analyzed. The research results can support demand response incentive subsidy price setting and demand response potential assessment.

Full immunization coverage in Pakistan remains suboptimal at 66%. An in-depth assessment is needed to understand the long-term trends in immunization and identify the extent of defaulters and associated risk factors of them being left uncovered by the immunization system. We conducted a 5-year analysis using the Government's Provincial Electronic Immunization Registry data for the 2018-2023 birth cohorts in Sindh province. We analyzed 8,792,392 child-level immunization records from 1 January 2018 to 31 May 2024 to examine trends in immunization coverage, timeliness, defaulter rates, and associated risk factors; Results: Our findings indicate gradual improvements in immunization coverage, with full immunization rates increasing by 23.2% (from 47.5% to 70.7%) from 2018 to 2022. While timeliness declined from 2018 to 2021, it recovered in 2022 and 2023. Over the 5-year study period, >90% of children defaulted on vaccinations, with 34.8% fully covered and 9.1% uncovered. Children from urban areas (OR = 1.54; 95% CI = 1.52, 1.56; p-value < 0.001) and those enrolled through fixed immunization sites (OR = 2.11; 95% CI = 2.08, 2.15; p-value < 0.001) and mobile immunization vans (OR = 1.13; 95% CI = 1.13, 1.77; p-value = 0.003) were at higher risk of being uncovered defaulters. This study demonstrates improvements in immunization coverage in Sindh while highlighting the challenge of low timeliness and high default rates. Our findings provide insights to strengthen immunization access and timeliness, particularly in high-default areas, and can guide policies in similar low-income settings for more equitable and comprehensive immunization coverage.

Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads

This paper studies the link between credit availability and student loan repayment using administrative federal student loan data. We demonstrate that expansions and contractions in federal student loan credit to institutions with high default rates explain most of the time series variation in student loan defaults between 1980 and 2010. Expansions in loan eligibility between 1976 and 1988 led to the entry of new, high-risk institutions, and default rates exceeding 30 percent in the late 1980s. Credit access was subsequently tightened through strict institutional and student accountability measures. This contracted credit availability at the highest default rate institutions, which in turn caused an exodus of institutions with high default rates, resulting in lower default rates on student loans. After 1992, the cycle was repeated, with credit access gradually loosened by unwinding many of the pre-1992 reforms. We confirm this time series narrative by examining discrete policy changes governing access to credit to show that tightening credit supply led to the closure of high-default schools and the relaxation of accountability rules resulted in their expansion. Our estimates imply that 85 percent of the increase in default between 1980 and 1990, and 95 percent of the decrease in default between 1990 and 2000 is driven by schools entering and exiting loan programs. One-third of the recent increase in default is associated with the entry of online programs following the relaxation of rules for lending to online schools, and another third is associated with the abolition of rules limiting the share of revenue coming from federal programs.