| 1. |
- Johansson, Alexander, et al.
(författare)
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Multi-Fleet Platoon Matching : A Game-Theoretic Approach
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 2980-2985
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Konferensbidrag (refereegranskat)abstract
- We consider the platoon matching problem for a set of trucks with the same origin, but different destinations. It is assumed that the vehicles benefit from traveling in a platoon for instance through reduced fuel consumption. The vehicles belong to different fleet owners and their strategic interaction is modeled as a non-cooperative game where the vehicle actions are their departure times. Each truck has a preferred departure time and its utility function is defined as the difference between its benefit from platooning and the cost of deviating from its preferred departure time. We show that the platoon matching game is an exact potential game. An algorithm based on best response dynamics is proposed for finding a Nash equilibrium of the game. At a Nash equilibrium, vehicles with the same departure time are matched to form a platoon. Finally, the total fuel reduction at the Nash equilibrium is studied and compared with that of a cooperative matching solution where a common utility function for all vehicles is optimized.
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| 2. |
- Masoumzadeh, Amin, et al.
(författare)
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Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets
- 2018
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Ingår i: IEEE Transactions on Power Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0885-8950 .- 1558-0679. ; 33:2, s. 1903-1914
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Tidskriftsartikel (refereegranskat)abstract
- Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50% price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.
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| 3. |
- Nekouei, Ehsan, et al.
(författare)
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Impact of quantized inter-agent communications on game-theoretic and distributed optimization algorithms
- 2018
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Ingår i: Uncertainty in Complex Networked Systems. - Cham : Birkhauser. ; , s. 501-532
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Bokkapitel (refereegranskat)abstract
- Quantized inter-agent communications in game-theoretic and distributed optimization algorithms generate uncertainty that affects the asymptotic and transient behavior of such algorithms. This chapter uses the information-theoretic notion of differential entropy power to establish universal bounds on the maximum exponential convergence rates of primal-dual and gradient-based Nash seeking algorithms under quantized communications. These bounds depend on the inter-agent data rate and the local behavior of the agents’ objective functions, and are independent of the quantizer structure. The presented results provide trade-offs between the speed of exponential convergence, the agents’ objective functions, the communication bit rates, and the number of agents and constraints. For the proposed Nash seeking algorithm, the transient performance is studied and an upper bound on the average time required to settle inside a specified ball around the Nash equilibrium is derived under uniform quantization. Furthermore, an upper bound on the probability that the agents’ actions lie outside this ball is established. This bound decays double exponentially with time.
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| 4. |
- Nekouei, Ehsan, et al.
(författare)
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Privacy of Information Sharing Schemes in a Cloud-based Multi-sensor Estimation Problem
- 2018
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Ingår i: 2018 Annual American Control Conference (ACC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 998-1002
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Konferensbidrag (refereegranskat)abstract
- In this paper, we consider a multi-sensor estimation problem wherein each sensor collects noisy information about its local process, which is only observed by that sensor, and a common process, which is simultaneously observed by all sensors. The objective is to assess the privacy level of (the local process of) each sensor while the common process is estimated using cloud computing technology. The privacy level of a sensor is defined as the conditional entropy of its local process given the shared information with the cloud. Two information sharing schemes are considered: a local scheme, and a global scheme. Under the local scheme, each sensor estimates the common process based on its measurement and transmits its estimate to a cloud. Under the global scheme, the cloud receives the sum of the sensors' measurements. It is shown that, in the local scheme, the privacy level of each sensor is always above a certain level which is characterized using Shannon's mutual information. It is also proved that this result becomes tight as the number of sensors increases. We also show that the global scheme is asymptotically private, i.e., the privacy loss of the global scheme decreases to zero at the rate of O(1/M) where M is the number of sensors.
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| 5. |
- Tanaka, Takashi, et al.
(författare)
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Linearly Solvable Mean-Field Road Traffic Games
- 2018
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Ingår i: 56th Annual Allerton Conference on Communication, Control, and Computing. - : IEEE. - 9781538665961 ; , s. 283-289
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Konferensbidrag (refereegranskat)abstract
- We analyze the behavior of a large number of strategic drivers traveling er an urban traffic network using the mean-field game framework. We sume an incentive mechanism for congestion mitigation under which each iver selecting a particular route is charged a tax penalty that is fine in the logarithm of the number of agents selecting the same ute. We show that the mean-field approximation of such a rge-population dynamic game leads to the so-called linearly solvable rkov decision process, implying that an open-loop epsilon-Nash uilibrium of the original game can be found simply by solving a nite-dimensional linear system.
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| 6. |
- Yoo, Jaehyun, et al.
(författare)
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Event-based Observer and MPC with Disturbance Attenuation using ERM Learning
- 2018
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Ingår i: 2018 European Control Conference, ECC 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783952426982 ; , s. 1894-1899
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Konferensbidrag (refereegranskat)abstract
- This paper presents a learning-based approach for disturbance attenuation for a non-linear dynamical system with event-based observer and model predictive control (MPC). Using the empirical risk minimization (ERM) method, we can obtain a learning error bound which is function of the number of samples, learning parameters, and model complexity. It enables us to analyze the closed-loop stability in terms of the learning property, where the state estimation error by the ERM learning is guaranteed to be bounded. Simulation results underline the learning's capability, the control performance and the event-triggering efficiency in comparison to the conventional event-triggered control scheme.
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