skip to main content

Title: Financial Risk-Based Scheduling of Micro grids Accompanied by Surveying the Influence of the Demand Response Program
This paper presents an optimization approach based on mixed-integer programming (MIP) to maximize the profit of the Microgrid (MG) while minimizing the risk in profit (RIP) in the presence of demand response program (DRP). RIP is defined as the risk of gaining less profit from the desired profit values. The uncertainties associated with the RESs and loads are modeled using normal, Beta, and Weibull distribution functions. The simulation studies are performed in GAMS and MATLAB for 5 random days of a year. Although DRP increases the total profit of the MG, it can also increase the risk. The simulation results show that RIP is reduced when downside risk constraint (DRC) is considered along with DRP implementation. Considering DRC significantly reduces the percentage of the risk while slightly decreasinz the profit.
Authors:
; ; ; ;
Award ID(s):
1757207
Publication Date:
NSF-PAR ID:
10230403
Journal Name:
2021 IEEE/IAS 57th Industrial and Commercial Power Systems Technical Conference (I&CPS)
Page Range or eLocation-ID:
1 to 9
Sponsoring Org:
National Science Foundation
More Like this
  1. Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperativemore »game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.« less
  2. Rising sea levels and the increased frequency of extreme events put coastal communities at serious risk. In response, shoreline armoring for stabilization has been widespread. However, this solution does not take the ecological aspects of the coasts into account. The “living shoreline” technique includes coastal ecology by incorporating natural habitat features, such as saltmarshes, into shoreline stabilization. However, the impacts of living shorelines on adjacent benthic communities, such as submersed aquatic vegetation (SAV), are not yet clear. In particular, while both marshes and SAV trap the sediment necessary for their resilience to environmental change, the synergies between the communities are not well-understood. To help quantify the ecological and protective (shoreline stabilization) aspects of living shorelines, we presented modeling results using the Delft3D-SWAN system on sediment transport between the created saltmarshes of the living shorelines and adjacent SAV in a subestuary of Chesapeake Bay. We used a double numerical approach to primarily validate deposition measurements made in the field and to further quantify the sediment balance between the two vegetation communities using an idealized model. This model used the same numerical domain with different wave heights, periods, and basin slopes and includes the presence of rip-rap, which is often used togethermore »with marsh plantings in living shorelines, to look at the influences of artificial structures on the sediment exchange between the plant communities. The results of this study indicated lower shear stress, lower erosion rates, and higher deposition rates within the SAV bed compared with the scenario with the marsh only, which helped stabilize bottom sediments by making the sediment balance positive in case of moderate wave climate (deposition within the two vegetations higher than the sediment loss). The presence of rip-rap resulted in a positive sediment balance, especially in the case of extreme events, where sediment balance was magnified. Overall, this study concluded that SAV helps stabilize bed level and shoreline, and rip-rap works better with extreme conditions, demonstrating how the right combination of natural and built solutions can work well in terms of ecology and coastal protection.« less
  3. We study economic incentives provided by space-time dynamics of day-ahead and real-time electricity markets. Specifically, we seek to analyze to what extent such dynamics promote decentralization of technologies for generation, consumption, and storage (which is essential to obtain a more flexible power grid). Incentives for decentralization are also of relevance given recent interest in the deployment of small-scale modular technologies (e.g., modular ammonia and biogas production systems). Our analysis is based on an asset placement problem that seeks to find optimal locations for generators and loads in the network that minimize profit risk. We show that an unconstrained version of this problem can be cast as an eigenvalue problem. Under this representation, optimal network allocations are eigenvectors of the space-time price covariance matrix while the eigenvalues are the associated profit variances. We also construct a more sophisticated placement formulation that captures different risk metrics and constraints on types of technologies to systematically analyze trade-offs in expected profit and risk. Our analysis reveals that space-time market dynamics provide significant incentives for decentralization and strategic asset placement but that full mitigation of risk is only possible through simultaneous investment in generation and loads (which can be achieved using batteries or microgrids).
  4. Abstract The 2018–2020 Ebola virus disease epidemic in Democratic Republic of the Congo (DRC) resulted in 3481 cases (probable and confirmed) and 2299 deaths. In this paper, we use a novel statistical method to analyze the individual-level incidence and hospitalization data on DRC Ebola victims. Our analysis suggests that an increase in the rate of quarantine and isolation that has shortened the infectiousness period by approximately one day during the epidemic’s third and final wave was likely responsible for the eventual containment of the outbreak. The analysis further reveals that the total effective population size or the average number of individuals at risk for the disease exposure in three epidemic waves over the period of 24 months was around 16,000–a much smaller number than previously estimated and likely an evidence of at least partial protection of the population at risk through ring vaccination and contact tracing as well as adherence to strict quarantine and isolation policies.
  5. Cyber insurance like other types of insurance is a method of risk transfer, where the insured pays a premium in exchange for coverage in the event of a loss. As a result of the reduced risk for the insured and the lack of information on the insurer’s side, the insured is generally inclined to lower its effort, leading to a worse state of security, a common phenomenon known as moral hazard. To mitigate moral hazard, a widely employed concept is premium discrimination, i.e., an agent/insured who exerts higher effort pays less premium. This, however, relies on the insurer’s ability to assess the effort exerted by the insured. In this paper, we study two methods of premium discrimination that rely on two different types of assessment: pre-screening and post-screening. Pre-screening occurs before the insured enters into a contract and can be done at the beginning of each contract period; the result of this process gives the insurer an estimated risk on the insured, which then determines the contract terms. The post-screening mechanism involves at least two contract periods whereby the second-period premium is increased if a loss event occurs during the first period. Prior work shows that both pre-screening and post-screeningmore »are generally effective in mitigating moral hazard and increasing the insured’s effort. The analysis in this study shows, however, that the conclusion becomes more nuanced when loss events are rare. Specifically, we show that post-screening is not effective at all with rare losses, while pre-screening can be an effective method when the agent perceives them as rarer than the insurer does; in this case pre-screening improves both the agent’s effort level and the insurer’s profit.« less