scholarly journals Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

2019 ◽  
Vol 9 (3) ◽  
pp. 576 ◽  
Author(s):  
Xiaofeng Liu ◽  
Bingtuan Gao ◽  
Yuanmei Li
Keyword(s):  
Incomplete Information ◽  
Demand Response ◽  
Electricity Consumption ◽  
Bidding Strategy ◽  
Auxiliary Equipment ◽  
Bayesian Game ◽  
Game Theoretic ◽  
Flexible Resource ◽  
Different Characteristics

Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users’ breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource’s characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.

scholarly journals Game-Theoretic Decision Support for Cyber Forensic Investigations

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5300
Author(s):  
Antonia Nisioti ◽  
George Loukas ◽  
Stefan Rass ◽  
Emmanouil Panaousis
Keyword(s):  
Cyber Security ◽  
Strategic Decisions ◽  
Bayesian Games ◽  
Bayesian Game ◽  
Cyber Forensics ◽  
Detection And Attribution ◽  
Different Types ◽  
Game Theoretic ◽  
The Cost

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players’ actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker’s type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.

scholarly journals Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling

2020 ◽  
Vol 10 (20) ◽  
pp. 7310
Author(s):  
Zhaofang Song ◽  
Jing Shi ◽  
Shujian Li ◽  
Zexu Chen ◽  
Wangwang Yang ◽  
...  
Keyword(s):  
Demand Response ◽  
Electricity Consumption ◽  
Uncertainty Modeling ◽  
Physical Models ◽  
Bidding Strategy ◽  
Willingness To Participate ◽  
Demand Side ◽  
Bidding Strategies ◽  
Optimal Bidding ◽  
Residential Consumers

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.

scholarly journals Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 808
Author(s):  
Andrew Blohm ◽  
Jaden Crawford ◽  
Steven A. Gabriel
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Wholesale Price ◽  
Electricity Consumption ◽  
Behavioral Changes ◽  
Electricity Prices ◽  
Reduce Electricity Consumption ◽  
Grid Operators ◽  
Residential Demand

Residential demand response (DR) programs are generally administered through an electricity distribution utility, or an electric grid operator. These programs typically reduce electricity consumption by inducing behavioral changes in the occupants of participating households. We propose implementing a wholesale-price-sensitive residential DR program through the retail electricity provider (REP), who has more naturally aligned incentives to avoid high wholesale electricity prices and maintain customer satisfaction, as compared to distribution utilities, grid operators, and the average residential consumer. Retail electricity providers who serve residential consumers are exposed to substantial price risk as they generally have a portion of their portfolio exposed to variable real-time wholesale electricity prices, despite charging their residential customers a fixed retail electricity price. Using Monte Carlo simulations, we demonstrate that demand response, executed through internet-connected thermostats, to shift real-time residential HVAC load in response to real-time prices, can be used as an effective physical hedge, which is both less costly and more effective than relying solely on financial hedging mechanisms. We find that on average a REP can avoid USD 62.07 annually per household using a load-shifting program. Given that REPs operate in a low margin industry, an annual avoided cost of this magnitude is not trivial.

scholarly journals Load Aggregator-Based Integrated Demand Response for Residential Smart Energy Hubs

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Wenjie Lv ◽  
Jian Wu ◽  
Zhao Luo ◽  
Min Ding ◽  
Xiang Jiang ◽  
...  
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Noncooperative Game ◽  
Real Time Scheduling ◽  
Maximal Profit ◽  
Time Scheduling ◽  
Flexible Loads ◽  
Flexible Resource

In order to attract more flexible resource to take part in integrated demand response (IDR), this can be realized by introducing load aggregator-based framework. In this paper, based on residential smart energy hubs (S.E. Hubs), a two-level IDR framework is proposed, in which S.E. Hub operators play the role of load aggregators. The framework includes day-ahead bidding and real-time scheduling. In day-ahead bidding, S.E. Hub operators have to compete dispatching amount for maximal profit; hence, noncooperative game approach is formulated to describe the competition behavior among operators. In real-time scheduling, the dispatching model is formulated to minimize the error between real-time scheduling amount and bidding amount. Moreover, in order to reduce the influence of IDR on residential users, 4 categories of users’ flexible loads are modeled according to load consumption characteristic, and then these models are considered as the constraints in real-time scheduling. A case study is designed to validate the effectiveness of the proposed two-level IDR framework. And simulation results confirm that smart grid, S.E. Hub operators, and residential users can benefit simultaneously.

scholarly journals Demand Response Programs Management in an Energy Community with Diversity of Appliances

2021 ◽  
Vol 239 ◽  
pp. 00023
Author(s):  
Rúben Barreto ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Demand Response ◽  
Electricity Consumption ◽  
Primary Objective ◽  
Demand Response Program ◽  
Energy Community ◽  
Demand Response Programs ◽  
The Given ◽  
Peak Set

This paper shows the behaviour of a Demand Response program designed to be implemented in Energy Communities, where they take advantage of photovoltaic production. The primary objective is to manage both photovoltaic overproduction and village consumption efficiently. The DR program focuses on looking for consecutive periods that exceed a target peak set by the aggregator after analysing the consumption of the given energy community. The case study includes three villages, where participants are expected to be members of a community. The results are that participants will see a reduction in costs and electricity consumption.

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