Optimal Energy Allocation Research Articles

Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations

Integrated demand response (IDR) is an important approach to promote renewable energy consumption and improve energy efficiency in integrated energy systems. In IDR, not only multi-energy users, but also demand-side energy stations, can become demand response resources. Hence, making full use of the flexibility of energy supply and the elasticity of electricity and heat demand is vital to the development of IDR. In this paper, based on the background of the electricity-heat integrated energy system, the precise models of typical controllable electric and heat load are constructed considering the user’s requirements for comfort. Based on the model, the electricity-heat coordinated retail market framework is proposed to achieve the coordinated clearing of electric and heat load, managing the district energy generation and consumption through transactive control methods. The bidding strategies for electric and heat load are established for customers to participate in the market. The market clearing rules are defined with the aim of maximizing the net revenue of integrated energy service agency to realize the optimal energy allocation of energy station devices. Finally, the case study demonstrates that the proposed method can optimize both the resource allocation of demand-side energy stations and the energy use of customers through economic means. About 80–90% of wind power can be absorbed through participation in market bidding, and the specific consumption rate depends on the type of energy stations and users’ demand. By guiding customers to consume energy reasonably during different market clearing periods, the energy expenditure can be reduced by 18.76% on average under the premise that the users’ comfort is guaranteed.

Machine learning based optimal renewable energy allocation in sustained wireless sensor networks

The environmental energy harvesting is adjudged as a reliable solution to power the wireless nodes for infinite time and assuring uninterrupted operation of deployed network nodes. But uncertain energy availability initiates an important research issue of energy management in rechargeable sensor nodes. An integrated approach of energy assignment principles with adaptive duty cycling has been proposed to efficiently utilize the available energy and to maximize the node performance. The R interface based machine learning ensemble approach has been used for solar irradiance prediction to pre-estimate the node duty cycle. Dynamic programming based optimization problem has been used for real time adaption of pre-computed node duty cycle. The effectiveness of proposed work has been validated using MATLAB interface by extensive simulations on real time solar energy profiles in terms of magnitude and stability of sensors average duty cycle. The proposed algorithm achieves an average duty cycle of 65% to 69% with a limit of 70% maximum duty cycle irrespective of irregular radiation patterns throughout the day as well as for different forecasting horizons. The results shows minimum variation in estimated and real time energy profiles in stable weather conditions and optimize the duty cycle in irregular weather conditions. The results also shows minimum variation ( $$>2\%$$ ) in estimated and real time energy profiles in stable weather conditions and optimize the duty cycle in irregular weather conditions.

Optimal Energy Allocation in Multisensor Estimation Over Wireless Channels Using Energy Harvesting and Sharing

We investigate the optimal power control for multisensor estimation of correlated random Gaussian sources. A group of wireless sensors obtains local measurements and transmits them to a remote fusion center (FC), which reconstructs the measurements using the minimum mean-square error estimator. All the sensors are equipped with an energy harvesting module and a transceiver unit for wireless, directed energy sharing between neighboring sensors. The sensor batteries are of finite storage capacity and prone to energy leakage. Our aim is to find optimal power control strategies, which determine the energies used to transmit data to the FC and shared between sensors, so as to minimize the long-term average distortion over an infinite horizon. We assume centralized causal information of the harvested energies and channel gains, which are generated by independent finite-state stationary Markov chains. The optimal power control policy is derived using a stochastic predictive control formulation. We also investigate the structure of the optimal solution, a Q-learning based suboptimal power control scheme and two computationally simple and easy-to-implement heuristic policies. Extensive numerical simulations illustrate the performance of the considered policies.

Effects of Jamming Attacks on a Control System With Energy Harvesting

We consider the problem of control and remote state estimation with battery constraints and energy harvesting at the sensor (transmitter) under DoS/jamming attacks. We derive the optimal non-causal energy allocation policy that depends on current properties of the channel and on future energy usage. The performance of this policy is analyzed under jamming attacks on the wireless channel, in which the assumed and the true channel gains differ, and we show that the resulting control cost is not monotonic with respect to the assumed channel gain used in the transmission policy. Additionally, we show that, in case there exists a stabilizing policy, then the optimal causal policy ensures stability of the estimation process. The results were illustrated for non-causal and causal energy allocation policies under different jamming attacks.

Collaborative Energy Management Optimization Toward a Green Energy Local Area Network

Rapid economic development has been observed worldwide, which has caused environmental problems to worsen. Thus, the Energy Internet (EI), which accesses renewable energy and provides high-quality power services, has recently become a hot issue. As a subnet of the EI, an energy local area network (ELAN) consists of renewable power generation equipment, controllable distributed power generation equipment, storage systems, electric vehicles, and a large number of loads. Energy management is required for economic, environmental, and safety considerations. This paper proposes an energy management optimization model that addresses ELAN operations and includes pollution treatment fees; this model provides intelligent control of the charging and discharging of plug-in hybrid electric vehicles (PHEVs). This model achieves a nonlinear energy management optimization for an ELAN. To promote optimal performance, an improved comprehensive learning particle swarm optimization (CLPSO) algorithm is presented; it combines Tabu Search (TS) and CLPSO to avoid local optima. To verify the performance of our model, two experimental scenarios are built. The simulation results show that our energy management optimization model fulfills the optimal allocation of energy and that the PHEV intelligent charging/discharging strategy promotes economic benefits for the network.

SF-OEAP: Starvation-Free Optimal Energy Allocation Policy in a Smart Distributed Multimicrogrid System

The advancements in the smart grid involves high penetration of renewable energy resources into distribution system by building a microgrid. However, the intermittent nature of the nondispatchable generation leads to the issue of demand supply mismatch in the microgrid system. It will be sensible for microgrids to trade energy with one another. This requires a smart distributor, which collects data regarding superfluous energy from providers and takes fair decision of microgrid-generated energy allocation among consumers. This paper presents a starvation-free optimal microgrid-generated energy allocation policy (SF-OEAP) in a smart distributed multimicrogrid system. A novel optimal energy allocation policy is proposed wherein, prediction accuracy of net load forecast and revenue index of consumer microgrid are considered as vital parameters. In a prioritized energy distribution mechanism, it may happen that the consumer microgrid can be starved of microgrid-generated energy. Therefore, in addition to vital parameters, starvation parameter is considered in designing energy allocation policy for sustaining the consumers in the smart distributed multimicrogrid system. Furthermore, the proposed scheme uses weights to control the importance of each of the parameters in the energy allocation policy. The proposed SF-OEAP is simulated and justified so that it is stable enough to apply in a real-time electricity market.

Dynamic programming based optimal renewable energy allocation in sustained wireless sensor networks

Due to limited energy, conventional battery powered sensor nodes have constraints to meet the present day demanding applications. Although energy harvesting techniques lead to sustained network operation, uncertain energy availability initiates an important research issue of energy management in rechargeable sensor nodes. In the proposed work, an integrated approach of energy assignment principles with adaptive duty cycling has been introduced to efficiently utilize the available energy and to maximize the node performance. The machine learning based Cubist model has been used to pre-estimate the node duty cycle and simulated on the R interface. Real time changes in the computed duty cycle have been implemented by the dynamic programming based adaptive duty cycle algorithm and simulated using the MATLAB interface. The effectiveness of the proposed work has been validated by extensive simulations on real time solar energy profiles in terms of magnitude and stability of sensor's average duty cycle. The proposed algorithm achieves an average duty cycle of 62% to 67% with a limit of 70% maximum duty cycle irrespective of irregular radiation patterns throughout the day as well as for different forecasting horizons. The results also show minimum variation in the estimated and real time energy profiles in stable weather conditions and optimize the duty cycle in irregular weather conditions.

Allocation of optimal energy in an energy-harvesting cooperative multi-band cognitive radio network

This paper studies the achievable total throughput of an energy harvesting cooperative cognitive radio (CR) network. A CR transmitter cooperates with a primary user (PU) transmission if PU is found to be present in the given channel while it transmits its own data in the absence of PU. The CR transmitter is an energy harvesting node which harvests simultaneously from non-RF signal as well as RF signal of PU. The CR transmitter uses the harvested energy on shared basis for cooperation and transmission. The same study is also extended for cooperation of multiple CRs in multiple PU band scenario. In cooperation in muti-band scenario, all CRs sense all PU channels and sensing informations are fused at fusion centre to know about the status of PU. Performance is investigated in terms of total network throughput for several parameters such as sensing time, energy splitting parameter, and energy allocation ratio etc. Novel analytical expressions for total useful throughput and optimal energy allocation ratio parameter under the considered network scenario are developed. Useful throughput and optimal energy allocation ratio parameter are also estimated for a target secondary network (CR network) throughput under a quality of service constraint of PU such as collision probability. It is observed that the value of optimal energy allocation parameter gets reduced if the number of CRs in cooperation increases or the number of PU channels increases.

Stochastic Games for the Smart Grid Energy Management With Prospect Prosumers

In this paper, the problem of the smart grid energy management under stochastic dynamics is investigated. In the considered model, at the demand side, it is assumed that customers can act as prosumers who own renewable energy sources and can both produce and consume energy. Due to the coupling between the prosumers’ decisions and the stochastic nature of renewable energy, the interaction among prosumers is formulated as a stochastic game, in which each prosumer seeks to maximize its payoff, in terms of revenues, by controlling its energy consumption and demand. In particular, the subjective behavior of prosumers is explicitly reflected into their payoff functions using the prospect theory, a powerful framework that allows modeling real-life human choices, rather than objective, user-agnostic decisions, as normative models do. For this prospect-based stochastic game, it is shown that there always exists a stationary Nash equilibrium where the prosumers’ trading policies in the equilibrium are independent of the time and their histories of the play. Moreover, to obtain one of such equilibrium policies, a novel distributed algorithm with no information sharing among prosumers is proposed and shown to converge to an $\epsilon$ -Nash equilibrium in which each prosumer is able to achieve its optimal payoff in an equilibrium up to a small additive error $\epsilon$ . On the other hand, at the supply side, the interaction between the utility company and the prosumers is formulated as an online optimization problem in which the utility company's goal is to learn its optimal energy allocation rules. For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori . Simulation results justify the convergence of the proposed algorithms and present new insights toward more efficient energy management in the smart grids.

Optimal Attack Energy Allocation against Remote State Estimation

Recently, the security issue of wireless cyber-physical systems (CPS) has attracted much attention from different communities. In this paper, we consider energy-efficient optimal attack power schedule against remote state estimation of wireless CPS under constrained denial-of-service attacks based on channels’ signal-to-interference-plus-noise ratio (SINR). We propose a wireless communication model with SINRs of channels, in which different attack powers can cause different dropout rates. The problem of optimal attack power schedule that causes the largest performance degradation of the remote state estimation, subject to attacker's average energy constraint in multisystems, is solved by formulating it as a Markov decision process. We show that an optimal deterministic and stationary policy exists and the optimal policy has a threshold structure.

Effect of energy allocation in dual‐hop communication systems with DF protocol

Relay communication has been receiving considerable attention for its advantage of extension in communication coverage and enhancement of communication performance. In this Letter, the author studied the performance of dual-hop communication systems with a decode-and-forward protocol. The author first analyses the performance of dual-hop communication systems with various modulation schemes. Then, the author found the optimum energy allocation depending on the relay location for each modulation scheme. Finally, the performance of dual-hop communication systems with equal energy allocation and optimum energy allocation was compared.

Nested multi-objective PSO for optimal allocation and sizing of renewable energy distributed generation

Distribution generation (DG) opened a new era for using renewable energy sources to face the future load expansion and enhance the stability of the power system. A methodology for allocating and sizing and analysis for renewable energy sources as DG sources are introduced. Allocation criteria for weaken bus-bars are introduced in this paper. The selected bus-bars are then supported with optimized DG sources in order to enhance the system capability to withstand any future expansion in load. The voltage weakening index is used to compare the voltage of the post-load increase with the base voltage at normal load. A new proposed nested particle swarm optimization (PSO) technique is introduced to design the optimal size of renewable energy source capacity. Two objective functions have been designed for optimal allocation and sizing of the DG; they are the generation and operation energy cost and the transmission line losses (TLLs). The allocation methodology is performed using multi-objective particle swarm optimization. Different scenarios for the optimal operation under different operating conditions are introduced. The new contribution of this paper is the use of the new nested PSO technique for optimal sizing taking the time variation into consideration which has not been revealed before in the literature. The results obtained using the new proposed optimization program show a great potential of deployment of DG renewable energy sources in terms of reducing the cost of energy and TLLs and improving the system operational conditions.

Towards the optimal operation of a thermal-recharging float in the ocean

A large number of profiling floats are currently in use around the world oceans to monitor oceanographic conditions. Generally, floats are in motion by the buoyancy force, and missions of data collection, storage, and communication are carried out relying on Lithium-ion batteries. Chao et al. developed the TREC float which is operated on batteries rechargeable from the ocean thermocline. It uses a patented TREC (Thermal RECharging) engine which is a thermo-mechano-electric converter utilizing a Phase Change Material (PCM) system for energy harvesting. Data are desired from a target area, but the floats have lacked a way to stay on target, carried away from it by ocean currents. An alternative solution is proposed in the present study combining a profile float (the TREC) with small thrusters to allow for location correction. The main purpose of this study is to solve an optimal energy allocation problem through the numerical analysis of the nonlinear correlation between energy harvesting and consumption. A powered TREC is designed by sizing a feasible thruster to translate back to the target area. A parameter study on energy allocation is carried out with various types of translation motion (vertical, horizontal, and rotational for Options V, H, and R), and the geometry of the float (spherical and cylindrical end shapes for Options S, and C). Option R is the most promising candidate under the design requirements, though it must be noted that the location correction phase takes much longer due to the rotation time.

Research and application of UHV power transmission in China

The power demand increases rapidly in China; however, the areas of huge power demands are of long distance from most areas of abundant energy resource in the country. Therefore, China put in great effort to develop ultrahigh voltage (UHV) power transmission systems to optimise its energy allocation. This includes (i) systematically developing key technologies such as overvoltage suppression, external insulation design, and electromagnetic environment control, and (ii) developing key equipment such as UHV alternative current (UHVAC) transformers, circuit breakers, and series compensated equipment, and UHV direct current (UHVDC) converter transformers, smoothing reactors, converter valves, and DC transmission control and protection systems. Eight AC UHV projects and 11 DC UHV projects have been built in China, which play an important role in the optimal allocation of energy. Plus there are one more UHVAC and three more UHVDC transmission projects in construction, while UHVAC gas-insulated lines will be applied in the UHVAC line crossover the Yangtze River and the ±1100 kV UHVDC power transmission technology is in development. Here, the development and application of UHV power transmission technologies in China are described, some main challenges the UHV projects encountered are discussed, and experiences obtained from the operation of UHV systems are introduced. It is concluded that China obtained mature experience in developing, constructing, and operating UHV systems and successfully realised long-distance, large-capacity power transmission, and the UHV power transmission technology has become an important measure for energy allocation in large areas.

Retrofitting the Regulated Power Plant: Optimizing Energy Allocation to Electricity Generation, Water Treatment, and Carbon Capture Processes at Coal-Fired Generating Facilities

Minimizing the human health and environmental impacts from electricity generation at existing coal-fired power plants (CFPPs) will require extensive plant retrofit, but the separations technologies for reducing CO2 and wastewater emissions at CFPPs are energy intensive. This paper quantifies the electricity generation efficiency and revenue implications of allocating electricity, steam, or residual heat to these emission control processes under several different regulatory scenarios. We develop an energy balance model of the National Energy Technology Laboratory’s 550 MW CFPP without carbon capture (CC) and add models of five CC technologies (one electricity driven and four thermal processes) and five wastewater treatment (WT) technologies (one electricity driven and four thermal processes) to comply with the Clean Power Plan and Effluent Limitation Guidelines emissions regulations. Plant revenue is maximized by utilizing residual heat for WT or CC, but the optimal allocation of limited residual heat reso...

Energy Scarcity Promotes a Brain-wide Sleep State Modulated by Insulin Signaling in C. elegans

SummaryNeural information processing entails a high energetic cost, but its maintenance is crucial for animal survival. However, the brain’s energy conservation strategies are incompletely understood. Employing functional brain-wide imaging and quantitative behavioral assays, we describe a neuronal strategy in Caenorhabditis elegans that balances energy availability and expenditure. Upon acute food deprivation, animals exhibit a transiently elevated state of arousal, indicated by foraging behaviors and increased responsiveness to food-related cues. In contrast, long-term starvation suppresses these behaviors and biases animals to intermittent sleep episodes. Brain-wide neuronal population dynamics, which are likely energetically costly but important for behavior, are robust to starvation while animals are awake. However, during starvation-induced sleep, brain dynamics are systemically downregulated. Neuromodulation via insulin-like signaling is required to transiently maintain the animals’ arousal state upon acute food deprivation. Our data suggest that the regulation of sleep and wakefulness supports optimal energy allocation.

Energy efficient policy selection in wireless sensor network using cross layer approach

The authors have addressed the issue of energy saving in packet retransmission by reducing the packet loss in the first attempt. They have formulated the problem as Markov decision process and mapped the system into various states based on system dynamics like channel gain, buffer space, battery energy and harvested energy. In wireless sensor network, adaptive policy management is the best approach to work out with the variation of system states in a dynamically changing environment. The policies are defined regarding various controllable parameters like transmission power, modulation scheme, and transmission rate. The solution is sought regarding best policy selection as per the individual state of the system. They have evaluated the performance of the proposed optimum transreceiver scheme by extensive simulations with existing schemes like optimum energy allocation, adaptive modulation and adaptive power management for the various performance metrics like net bit rate and energy consumption in retransmission of packets for different system states.

Reinforcement Learning-based Real-time Energy Management for Plug-in Hybrid Electric Vehicle with Hybrid Energy Storage System

Energy allocation is a crucial issue for the energy storage system(ESS) of a plug-in hybrid electric vehicle (PHEV).In this paper, in order to realize an optimal energy allocation between the battery and the ultracapacitor in an ESS, a reinforcement learning-based real-time energy-management strategy was proposed. Firstly, a long driving condition which included various speed variations was chosen and the power transition probability matrices based Markov chain were calculated. Then, the reinforcement learning algorithm was used to obtain a control strategy aiming at minimizing the energy loss of the energy storage system. To use effectively the control strategy, the power transition probability matrices needed updating because the validation driving condition was different from the calculated driving condition and Kullback-Leibler(KL) divergence can be used to determine when the updating happened. At the same time, the updating-online control strategy was applied to the validation driving condition. Finally a comparison among the online energy management, offline energy management and the dynamic programming-based energy management was shown and the results indicate that the RL-based real-time energy-management strategy can decrease the energy loss and can be employed in real-time.

Energy allocation optimization for AF multi-hop in a cognitive radio system

In this paper, novel optimal energy allocation schemes for the secondary users in an amplify-and-forward multi-hop underlay cognitive network are proposed. The optimization problem is formulated as a maximization of the instantaneous received signal to noise ratio, under interference power constraints that are imposed to protect the primary network. First, a novel geometrical approach is proposed for the two and three hop cases. Simulations show that the proposed approach combined with adaptive modulation outperforms the cooperative cognitive system with uniform energy distribution. Then, a Lagrange-based analytical approach solution is proposed to the problem for the 2-hop case. Numerical results show that the Lagrangian resolution leads to the same results as the geometrical one. The advantage of the geometrical approach is to get more insight for the 2-hop case and makes the resolution tractable for more hops in the network.

Explaining sex differences in lifespan in terms of optimal energy allocation in the baboon.

We provide a quantitative test of the hypothesis that sex role specialization may account for sex differences in lifespan in baboons if such specialization causes the dependency of fitness upon longevity, and consequently the optimal resolution to an energetic trade-off between somatic maintenance and other physiological functions, to differ between males and females. We present a model in which females provide all offspring care and males compete for access to reproductive females and in which the partitioning of available energy between the competing fitness-enhancing functions of growth, maintenance, and reproduction is modeled as a dynamic behavioral game, with the optimal decision for each individual depending upon his/her state and the behavior of other members of the population. Our model replicates the sexual dimorphism in body size and sex differences in longevity and reproductive scheduling seen in natural populations of baboons. We show that this outcome is generally robust to perturbations in model parameters, an important finding given that the same behavior is seen across multiple populations and species in the wild. This supports the idea that sex differences in longevity result from differences in the value of somatic maintenance relative to other fitness-enhancing functions in keeping with the disposable soma theory.