Tertiary Control for Energy Management of EV Charging Station Integrated With PV and Energy Storage

Along with the rapid increase in the number of electric vehicles, more and more EV charging stations tend to have charging infrastructure, rooftop photovoltaic and energy storage all together for energy saving and emission reduction. Compared with individual design for each of the components in such kind of systems, an integrated design can result in higher efficiency, increased reliability, and lower total capital cost. This paper mainly focuses on the tertiary control strategy for dynamic state operation, such as PV generation fluctuation and random arrival/leave of EVs. The tertiary control aims to achieve stable operation under dynamic states, as well as to optimize the energy flow in the station to realize maximal operational benefits with constraints such as peak/valley price of electricity, state of discharge limitation of battery, etc. In this paper, four energy management functions in tertiary control level are proposed, and their performance is verified by simulations. By using prediction of PV power and EV load in the following 72 h, a novel tertiary control logic is proposed to optimize PVC and ESC power flow by changing their droop characteristics, so that minimum operational cost for the station can be achieved. Furthermore, a sensitivity analysis is conducted for three parameters, including ES battery capacity, weather influence, and PV and EV load prediction error. The results from sensitivity analysis indicate that ES battery capacity and weather condition lead to a great impact on the operational cost of the integrated charging station, while a typical prediction error of PV power and EV load will not influence the optimization result significantly.

Smart DC Microgrid

The objective of this chapter is to elucidate on microgrid technologies, a comparison of direct current (DC) microgrid technology and alternating current (AC) microgrid technology, the role of the information and communication technology, demand response programs, and the evolution of Industry 4.0 in detail. The microgrid is a cyber-physical system. ICT is used for computing control algorithms and sending control information to actuators for physical processes. In a cyber-physical system, the physical processes, which are governed by the laws of physics, are controlled by computers. The computers are used for computing or executing the algorithms (i.e., the control logic) and the result is sent to the actuators in the form of control signal for actual control. In a microgrid, a consumer can act as a producer also, which is termed as the prosumer. This chapter explains the maximum power point tracking algorithm, software-defined battery, the operation of parallel converters, the working of prosumer, the demand response program, communication technologies, and the (industrial) Internet of Things.

Khurais HP Gas Compressor Swing Line Control Logic Modification Through Dynamic Simulation Modelling

Khurais central processing Facility is a hydrocarbon plant consists of 5 GOSPs with a stabilizer (i.e oil train), 3 gas plants and power plant with their associated utilities. Each oil train has one HP gas compressor to send the gas from oil trains to gas plant. These compressors is oversized and running each compressor alone results in excessive recycling and wasting energy. In 2018 Khurais introduced a new energy optimization initiative which is swing line that is a common header connects all HP compressors together with the objective of maximizing operating loads in each gas compressor. In doing so, the last compressor can be shut down for as long as the capacity of the other compressors is still adequate to accommodate the extra gas rate from the shutdown compressor.[1] (Refer to Figure 2) Although this approach resulted in maximizing energy efficiency of the facility it introduced a new challenge to the facility which is in case of any trip to a compressor connected to swing line all other connected compressors will trip. Recently Khurais introduce a new philosophy to shift the load from tripped compressors to other compressors through modifying swing line and compressors DCS control logics. This paper covers the swing-line control logic modifications and enhancement followed by Saudi Aramco Khurais producing facility to overcome multiple compressor outages challenge. This paper is aimed to: Share the challenge of multiple compressors trips with swing line operations that occurs if one of the compressors connected to swing line is tripped resulting in 100% of gas flaring. Present dynamic simulation modelling results and new logic modifications implemented for compressor controls to avoid such multiple trips scenario. Present Compressor trips cases before and after implementing the control logic enhancements. Highlight how this approach enhanced compressor's reliability and helped Saudi Aramco to reduce its carbon footprint. In this paper various dynamic simulation modelling graphs that showed the root cause for multiple compressor trips will be presented, evaluation of several control logic and their consequences. This topic will also cover the new proposed logic with zero investment to avoid multiple compressors trip scenario with operations of swing line. Details about behavior of compressor before and after the modification including the reaction of newly implemented control logic in flare control valves is included part of this paper. After the implementation of this logic the following results have been realized: No incident of multiple compressor trips happened due to proper functioning of logic. Eliminated 60-100 MMSCF/year of flaring due to multiple compressors trips scenario through having "quickest" handle to restoring flow to the swing line and stop flaring. Reducing carbon footprints due to flaring compared to old setup. Realized Recovered revenue worth approximately US$ 500,000/year The novelty in this paper is describing an approach of gradual load shifting from a tripped compressor to other running compressors to avoid multiple compressors trips which can be utilized in any facility that has a common suction header connects all its compressors. Such logics can be implemented in-house with no modification in compressors or swing lines or other equipment.

Application of Multi-Agent Deep Reinforcement Learning to Optimize Real-World Traffic Signal Controls

Increasing traffic congestion leads to significant costs associated by additional travel delays, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today's traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that could operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, most of these proposed approaches were validated using artificial traffic grids. This paper therefore presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, currently employed in the real world at the studied intersections, are integrated in the traffic model with LISA+ and serve as a performance baseline. Our performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach in LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.

Application of Multi-Agent Deep Reinforcement Learning to Optimize Real-World Traffic Signal Controls

Increasing traffic congestion leads to significant costs associated by additional travel delays, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today's traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that could operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, most of these proposed approaches were validated using artificial traffic grids. This paper therefore presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, currently employed in the real world at the studied intersections, are integrated in the traffic model with LISA+ and serve as a performance baseline. Our performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach in LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.

Series FACTS controllers in industrial low voltage electrical distribution networks for reducing fault current levels

In this paper, series FACTS devices like Thyristor control series capacitor(TCSC)and Static synchronous series compensator (SSSC) with designed control logic used to reduce the fault current located in LV distribution network at the LV busbar. The electrical distribution network in small and medium scale industries such as steel plants, process and power plants is through low voltage switchgear (LVS) fed from motor control centre (MCC) switchgear through step down transformer of 11kV or 33kV /415V. The designed switchgear in the LV side for these utilities usually is at 50kA. However, the process loads are continuously increasing and sustained with additional feeders with the existing switchgear. Consequently, the fault current at the busbar of the switchgear increases which may require the replacement of entire switchgear to the new design fault current. However, upgrading the existing switchgear is not an economical solution to the industries. Alternatively reducing the fault current at the busbar is feasible. Controller design implemented for reducing the short circuit current with series FACTS devices. A study carried on 800 MW Thermal power plant Ash handling LVS in ETAP and Matlab. It is observed that the results are encouraging to use series FACTS devices effectively in the LVS.

Access Control Models

Access control is a part of the security of information technologies. Access control regulates the access requests to system resources. The access control logic is formalized in models. Many access control models exist. They vary in their design, components, policies and areas of application. With the developing of information technologies, more complex access control models have been created. This paper is concerned with overview and analysis for a number of access control models. First, an overview of access control models is presented. Second, they are analyzed and compared by a number of parameters: storing the identity of the user, delegation of trust, fine-grained policies, flexibility, object-versioning, scalability, using time in policies, structure, trustworthiness, workflow control, areas of application etc. Some of these parameters describe the access control models, while other parameters are important characteristics and components of these models. The results of the comparative analysis are presented in tables. Prospects of development of new models are specified.

My mind is not the universe – where/what is the universe?

In order to describe my findings/conclusions systematically, a new semantic system (i.e., a new language) has to be intentionally defined by the present article. Humans are limited in what they know by the technical limitation of their cortical language network. The conventionally-called “physical/objective reality” around my conventionally-called “physical/objective body” is actually a geometric mathematical model (being generated/mathematically-modeled by my brain) – it's actually a subset/component/part/element of my brain’s mind/consciousness. A reality is a situation model (SM). Our universe is an autonomous objective parallel computing automaton (aka state machine) which evolves by itself automatically/unintentionally – wave-particle duality and Heisenberg’s uncertainty principle can be explained under this SM of my brain. Each elementary particle (as a building block of our universe) is an autonomous mathematical entity itself (i.e., a thing in itself). Our universe has the same nature as a Game of Life system – both are autonomous objective parallel-computing automata. If we are happy to accept randomness, then it is obviously possible that all other worlds in the many-worlds interpretation do not exist objectively. The conventionally-called “space” does not exist objectively. “Time” and “matter” are not physical. Consciousness is the subjective-form (aka quale) of the mathematical models (of the objective universe) which are intracorporeally/subjectively used by the control logic of a Turing machine’s program directly-fatedly. A Turing machine’s consciousness or deliberate decisions/choices should not be able to actually/objectively change/control/drive the (autonomous or directly-fated) worldline of any elementary particle within this world. Besides the Schrodinger equation (or another mathematical equation/function which is yet to be discovered) which is a valid/correct/factual causality of our universe, every other causality (of our universe) is either invalid/incorrect/counterfactual or can be proved by deductive inference based on the Schrodinger equation (or the aforementioned yet-to-be-discovered mathematical equation/function) only. Consciousness plays no causal role (“epiphenomenalism”), or in other words, any cognitive/behavioural activity can in principle be carried out without consciousness (“conscious inessentialism”). If the “loop quantum gravity” theory is correct, then time/space does not actually/objectively exist in the objective-evolution of the objective universe, or in other words, we should not use the subjective/mental concept of “time”, “state” or “space” to describe/imagine the objective-evolution of our universe.

Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology

The performance of hybrid electric vehicles (HEVs) greatly depends on the various sub-system components and their architecture, and designers need comprehensive reviews of HEVs before vehicle investigation and manufacturing. Simulations facilitate development of virtual prototypes that make it possible to rapidly see the effects of design modifications, avoiding the need to manufacture multiple expensive physical prototypes. To achieve the required levels of emissions and hardware costs, designers must use control strategies and tools such as computational modeling and optimization. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. With this motivation, the author developed a hybrid simulation tool with a rule-based topology. The major advantage of this tool is enhanced flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool provides the user with the ability to modify any sub-system according to one’s own requirements. In addition, the author introduces a simple logic control for a rule-base strategy as an example to show the flexibility of the tool in allowing the adaptation of any logic algorithm by the user. The results match the experimental data quite well. Details regarding modeling principle and control logic are provided for the user’s benefit.

My mind is not the universe – where/what is the universe?

In order to describe my findings/conclusions systematically, a new semantic system (i.e., a new language) has to be intentionally defined by the present article. Humans are limited in what they know by the technical limitation of their cortical language network. The conventionally-called “physical/objective reality” around my conventionally-called “physical/objective body” is actually a geometric mathematical model (being generated/mathematically-modeled by my brain) – it's actually a subset/component/part/element of my brain’s mind/consciousness. A reality is a situation model (SM). Our universe is an autonomous objective parallel computing automaton (aka state machine) which evolves by itself automatically/unintentionally – wave-particle duality and Heisenberg’s uncertainty principle can be explained under this SM of my brain. Each elementary particle (as a building block of our universe) is an autonomous mathematical entity itself (i.e., a thing in itself). Our universe has the same nature as a Game of Life system – both are autonomous objective parallel-computing automata. If we are happy to accept randomness, then it is obviously possible that all other worlds in the many-worlds interpretation do not exist objectively. The conventionally-called “space” does not exist objectively. “Time” and “matter” are not physical. Consciousness is the subjective-form (aka quale) of the mathematical models (of the objective universe) which are intracorporeally/subjectively used by the control logic of a Turing machine’s program directly-fatedly. A Turing machine’s consciousness or deliberate decisions/choices should not be able to actually/objectively change/control/drive the (autonomous or directly-fated) worldline of any elementary particle within this world. Besides the Schrodinger equation (or another mathematical equation/function which is yet to be discovered) which is a valid/correct/factual causality of our universe, every other causality (of our universe) is either invalid/incorrect/counterfactual or can be proved by deductive inference based on the Schrodinger equation (or the aforementioned yet-to-be-discovered mathematical equation/function) only. Consciousness plays no causal role (“epiphenomenalism”), or in other words, any cognitive/behavioural activity can in principle be carried out without consciousness (“conscious inessentialism”). If the “loop quantum gravity” theory is correct, then time/space does not actually/objectively exist in the objective-evolution of the objective universe, or in other words, we should not use the subjective/mental concept of “time”, “state” or “space” to describe/imagine the objective-evolution of our universe.