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1. Financial Risk-Based Scheduling of Micro grids Accompanied by Surveying the Influence of the Demand Response Program

   https://doi.org/10.1109/icps51807.2021.9416627  

   Khalili, Tohid; Ganjehlou, Hamed Ganjeh; Bidram, Ali; Nojavan, Sayyad; Asadi, Somayeh (April 2021, 2021 IEEE/IAS 57th Industrial and Commercial Power Systems Technical Conference (I&CPS))

   null (Ed.)

   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. 

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2. Kalman Filter Based Electricity Market States Forecasting: A State-Space Framework

   Rana, Md Masud; Bo, Rui; Choi, Bong Jun (August 2019, Proceedings of 9th IEEE International Conference on CYBER Technology in Automation, Control and Intelligent Systems (IEEE-CYBER 2019))

   Gaining money and high profit is the dream of electricity market investors; however, it requires accurate financial knowledge and price forecasting ability. Most of the investors are used the electricity market historical information for forecasting power generation, consumption, and utility price. Unfortunately, electricity market time-series profile is high volatility and change over time, so the factual data cannot accurately reflect the electricity market states such as power consumption and generation. In the literature, there is no systematic way or suitable models that can fit, analyze, and predict electricity market system states over time. Interestingly, this paper proposes an electricity market state-space model which is obtained by a set of electricity market differential equations. After simplifying of these equations, the continuous-time electricity market state-space model is derived. Using discrete-time step size parameter, the continuous-time system is discretised. Furthermore, the noisy measurements are obtained by a set of smart sensors. Finally, the Kalmna filter based electricity market state forecasting algorithm is developed based on noisy measurements. Simulation results show that the proposed algorithm can properly forecast the electricity market states. Consequently, this kind of model and algorithm can help to develop the electricity market simulator and assist investor to participate/invest electricity market regardless of the world economic downtown. 

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3. Solving dynamic programming with supremum terms in the objective and application to optimal battery scheduling for electricity consumers subject to demand charges

   https://doi.org/10.1109/CDC.2017.8263838  

   Jones, Morgan; Peet, Matthew M. (December 2017, IEEE Conference on Decision and Control)

   In this paper, we consider the problem of dynamic programming when supremum terms appear in the objective function. Such terms can represent overhead costs associated with the underlying state variables. Specifically, this form of optimization problem can be used to represent optimal scheduling of batteries such as the Tesla Powerwall for electrical consumers subject to demand charges - a charge based on the maximum rate of electricity consumption. These demand charges reflect the cost to the utility of building and maintaining generating capacity. Unfortunately, we show that dynamic programming problems with supremum terms do not satisfy the principle of optimality. However, we also show that the supremum is a special case of the class of forward separable objective functions. To solve the dynamic programming problem, we propose a general class of optimization problems with forward separable objectives. We then show that for any problem in this class, there exists an augmented-state dynamic programming problem which satisfies the principle of optimality and the solutions to which yield solutions to the original forward separable problem. We further generalize this approach to stochastic dynamic programming problems and apply the results to the problem of optimal battery scheduling with demand charges using a data-based stochastic model for electricity usage and solar generation by the consumer. 

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4. Transactive Control Approach to Trip Optimization in Electric Railways

   D'Achiardi, David (December 2019, 2019 IEEE 58th Conference on Decision and Control)

   Dynamic trip optimization in electric rail networks is a relatively unexplored topic. In this paper, we propose a transactive controller that includes an optimization framework and a control algorithm that enable minimum cost operation of an electric rail network. The optimization framework attempts to minimize the operational costs for a given electricity price by allowing variations of the trains’ acceleration profiles and therefore their power consumption and energy costs. Constraints imposed by the train dynamics, their schedules, and power consumption are included in this framework. A control algorithm is then proposed to optimize the electricity price through an iterative procedure that combines the desired demand profiles obtained from the optimization framework together with the variations in Distributed Energy Resources (DERs) while ensuring power balance. Together, they form to an overall framework that yields the desired transactions between the railway and power grid infrastructures. This approach is validated using simulation studies of the Southbound Amtrak service along the Northeast Corridor (NEC) between Boston, MA and New Haven, CT in the United States, reducing energy costs by 10% when compared to standard trip optimization based on minimum work. 

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5. Implementation of CVR in Distribution Networks by Optimal Coordination of BESS and PV Inverters Using Arithmetic Optimization Algorithm

   https://doi.org/10.1109/NAPS52732.2021.9654489  

   Ahanch, Mojtaba; McCann, Roy (November 2021, IEEE Green Technologies Conference (GreenTech))

   This article proposes a new framework for the substation demand reduction and power loss minimization in distribution networks by implementing conservation voltage reduction (CVR) strategy. The proposed framework coordinates Battery Energy Storage Systems (BESS), Smart PV inverters and voltage control devices -including OLTC and voltage regulators- so that the substation demand and network power loss are reduced while the service voltage range meets the IEEE 1547 standard (120-114 V). The suggested CVR strategy is applied to the IEEE 34-bus case study system consisting of two PV generations and BESS. The smart PV inverters are controlled based on the combined Volt/VArVolt/Watt (VVW) characteristics scheme. Also, BESS is charged and discharged with regard to the time and peaks have control modes, respectively. The Arithmetic Optimization Algorithm (AOA) is implemented in MATLAB scripts for solving the optimization problem. Power flow studies are carried out using OpenDSS software. Results reveal that the new framework can achieve higher substation demand reduction considering the concurrent control of PVs and BESS. 

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