Real-time pricing for smart grid with multi-energy microgrids and uncertain loads: a bilevel programming method

Electronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

Renewable Energies play crucial role in modern distribution systems. An active distribution system consists of several Micro-Grids (MGs) each of which has kinds of Distributed Generations (DGs) and Demand Response (DR) resources. Hence MG optimal operation is bi-level optimization problem whose upper level seeks for micro-grid owner profit and the lower level corresponds to distribution companies (DC) profit. In such a system MGs can exchange power between each other as well as distribution company. Therefore, upper level problem determines DGs generation, curtailed load, power exchange with other MGs subject to load balance constraint. Also Distribution Company (DISCO) clears retail market. On the other hand, renewable energies uncertainty and variably makes the MGs operation more complicated. In this paper, renewable energy variability handled by shift able load management and their uncertainty has been managed by curtailable load. The developed model has been solved by Particle Swarm Optimization (PSO) for upper level and then with given population in each iteration, lower level problem has been solved completely by other PSO package. This process ends when upper level PSO converge. Numerical studies have been shown that renewable management by proposed model leads to more optimal solutions.

This paper addresses the stochastic optimal day-ahead microgrid (MG) energy resources scheduling, considering the uncertain load, price of electricity, and generated electrical power by wind and solar units. Moreover, the vehicle-to-grid (V2G) implementation, load curtailment cost, and spinning reserve requirements are modeled to make the results more practical and applicable. Furthermore, the price elasticity of supply is considered to explore the relation between V2G capability and the optimization process. The stochastic energy resources scheduling problem is formulated in a two-level optimization framework. The unit commitment of dispatchable resources is analyzed in the upper level, and the lower level is formulated as a scenario-based two-stage stochastic programming problem that minimizes the operation cost of MG considering all the constraints. The risk of attaining unfavorable high costs of MG scheduling is considered using the variance approach. The generated scenarios are reduced by using the backward reduction method for each uncertain variable at each hour, independently. The artificial intelligent-based methods, including differential evolution algorithm (DEA), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES) are applied to solve the problem. The effectiveness of the proposed approaches is confirmed by simulations on a modified 13-bus IEEE test system, in two cases of neglecting the risk and including the managed risk by applying the real-world data. The results confirmed the better performance of CMAES for solving such optimization problems.

This paper proposes an interactive fuzzy programming method for seeking a satisfactory solution for multiobjective two‐level linear fractional programming problems in which the decision makers in the upper and lower levels have several objectives, by first setting up the fuzzy goals for several objectives of each decision maker and seeking a satisfactory solution for the degree of satisfaction of two decision makers in a cooperative manner. In the proposed method, the decision maker at the upper level sets the minimal satisfactory levels for each fuzzy goal and the decision maker at the lower level determines the aspiration levels. The minimal satisfactory levels are treated as a constraint and the solution closest to the aspiration levels of the decision maker at the lower level is computed. The ratio of the aggregative degrees of satisfaction of the decision makers in the upper and lower levels with the obtained solution is evaluated by using partial information on the preference of the decision makers. If the ratio of the degrees of satisfaction satisfies the given condition and the decision maker at the upper level is satisfied with this solution, then the interaction is complete. Otherwise, a satisfactory solution is sought by updating the minimal satisfactory and aspiration levels. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(9): 54–62, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10047

Traditionally, the economic operation of a microgrid (MG) is independent with that of a distribution network (DN), and the electricity trade between them is simply based on contract prices or wholesale electricity market prices. In order to coordinate the benefits of DNs and MGs more effectively, a new economic operation model for DNs with MG is proposed in this paper modeled as a bilevel programming problem (BLPP). At the upper level, electric power from the gird and electricity price of MGs are control variables. A DN seeks the least operation cost by optimizing these control variables. At the lower level, control variables are electric power of dispatchable distributed generations (DGs) and the DN. Based on the electricity price got from the upper level and the operation costs of dispatchable DGs, a MG minimizes its hourly operation cost by optimizing the control variables. Furthermore, electricity price constraints are considered in this BLPP model, and a method to dynamically determine the constraints is proposed. They are decided by the load power demand profile in the DN and the price differential between the wholesale electricity market and dispatchable DGs. A comprehensive real-time pricing for residential consumers is also considered in the operation model to study the impact from demand response. The proposed BLPP model is solved by particles swarm optimization based on bilevel iterative algorithm. The demonstration is performed on a modified IEEE 33-node radial distribution system.

During the past few years, microgrids (MGs) have been becoming more attractive as effective means to integrate different distributed energy resources (DERs). To coordinate active and reactive power sharing among DERs, conventional droop control method is widely used as a decentralized control scheme. However, sharing powers among the sources based on the units' rated capacities is not an optimal solution in terms of economy and efficiency. In this paper, a new adaptive droop-based control strategy is proposed for AC MGs to optimally share MG load between corresponding units. The mentioned control strategy is developed in two levels. The upper control level is a mixed-objective optimization algorithm that provides optimal set-points for power generations considering system's constraints and goals, while the lower control level is responsible for tracking the reference signals coming from the upper level. To demonstrate the effectiveness of the proposed control strategy under different operating scenarios, simulation results in a benchmark MG are also presented.

This paper describes the structure and performance of a 25-micron pitch 640 x 480 pixel uncooled infrared focal plane array (IR FPA) with silicon-on-insulator (SOI) diode detectors. The uncooled IR FPA is a thermal type FPA that has a temperature sensor of single crystal PN junction diodes formed in an SOI layer. In the conventional pixel structure, the temperature sensor and two support legs for thermal isolation are made in the lower level of the pixel, and an IR absorbing structure is made in the upper pixel level to cover almost the entire pixel area. The IR absorption utilizes IR reflections from the lower level. Since the reflection from the support leg portions is not perfect due to the slits in the metal reflector, the reflection becomes smaller as the support leg section increases in reduced pixel pitches. In order to achieve high thermal isolation and high IR absorption simultaneously, we have developed a new pixel structure that has an independent IR reflector between the lower and upper levels. The structure assures perfect IR reflection and thus improves IR absorption. The FPA shows a noise equivalent temperature difference (NETD) of 40 mK (f/1.0) and a responsivity non-uniformity of less than 0.9%. The good uniformity is due to the high uniformity of the electrical characteristics of SOI diodes made of single crystal silicon (Si). We have confirmed that the SOI diodes architecture is suitable for large format uncooled IR FPAs.

Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response

Community microgrids, as an emerging technology, offer resiliency in operation for smart grids. Microgrids are seeing an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can easily overwhelm such electric networks. In this work, we propose an efficient demand response (DR) scheme based on dynamic pricing to enhance the capacity of the microgrid to securely host a large number of EVs. A hierarchical two-level optimization framework is introduced to realize the DR scheme. At the upper level, the dynamic prices for the participating users in DR are optimized while at the lower level, each user optimizes its energy consumption based on the price signal from the upper level. An evolutionary algorithm and a mixed-integer linear programming model is employed to solve the upper and lower level problems, respectively. Energy scheduling problems of the users are solved in a distributed manner which adds to the scalability of the approach. The proposed DR scheme is tested on a microgrid system adopted from the IEEE European low-voltage distribution network. Numerical experiments confirm the effectiveness of the proposed DR scheme compared to the benchmark pricing policies from the literature.

The ghost crab Ocypode quadrata plays an important role in energy transfer between trophic levels, and has been widely used in evaluations of impacted environments. In order to provide data on the biology of this potential bioindicator species, the population structure and vertical distribution of individuals were studied on two beaches in southeastern Brazil. Each beach was divided into quadrants of 1000 m2 with boundaries of upper, middle and lower levels in relation to the waterline. Collected monthly by active searching through one year, the specimens of O. quadrata were sexed, measured for carapace width, and returned to the beach. Of the total of 1904 specimens collected, the largest proportion (46.2%) were males, followed by 31.4% juveniles. The vertical distribution of the ghost crabs differed among age groups: males mostly occupied the middle and upper levels; adult females, ovigerous or not, were more abundant in the lower level; and juveniles were evenly distributed in all levels, with a slight tendency toward the middle. The sex ratio favored males in a few months of the year and in the larger size classes. The abundance of O. quadrata is limited by low temperatures, and its spatial and temporal distribution is controlled by food availability and ease of reproduction. Knowledge of the biology of these crabs is essential in order to use them as a bioindicator species; the vertical distribution patterns may reflect changes in the beach hydrodynamics or other environmental factors.

This study presents for the first time the factors governing the recruitment in a rocky intertidal community of the Beagle Channel, Tierra del Fuego (54°51′S 68°29′W), Argentina. The aim of this study was to examine the effect of grazers and predators, free substrate availability and crustose coralline algae on the recruitment of the main sessile components of the intertidal:Notochthamalus scabrosus, Notobalanus flosculus, Mytilus chilensis, Perumytilus purpuratusandAulacomya atraat three intertidal levels. For barnacles, the probability of recruitment was higher with grazers, while the contrary was observed for bivalves. The number ofN. flosculusrecruits was higher with increased substrate availability, whileN. scabrosusrecruited more with reduced free substrate in the first sampling. Mussel recruitment was higher with reduced free substrate. The highest probability of recruitment ofN. scabrosuswas observed at the upper level. Notably, this probability and the recruits per plot were higher at the mid level under uncaged-ORP treatment than expected for the mid level. The probability of bivalve andN. flosculusrecruitment was higher at upper and lower levels, respectively. At the lower level, barnacle recruitment was higher on bare rock than on crustose coralline algae. Our results suggest that grazers increase the probability of barnacle recruitment, while the presence of sessile organisms enhances the density of mussel recruits. Almost no recruitment of bivalves was observed in ORPs over one year, showing that the secondary succession is slow in this environment.

Eddy-covariance observations above the densely built-up Centre of Nanjing were made from December 2011 to August 2012. Separate eddy-covariance systems installed at two levels on a 36-m tower located on a rooftop were operated simultaneously, and observations grouped into two sectors (A, B) according to the prevalent wind directions. For sector A, where the nearby buildings are all below the lower measurement level, the sensible heat and momentum fluxes are generally greater at the upper level. For sector B, where several high-rise buildings are located upwind, the sensible heat and momentum fluxes at the upper level are close to those at the lower level. The analysis shows that the turbulent eddy characteristics differ between the two wind sectors, leading to a different behaviour of turbulent exchange between the two levels. A hypothesis is proposed that addresses the vertical variation of turbulent fluxes in the urban roughness sublayer (RSL). For sector A, the buildings block the flow, change the trajectory of scalars, and distort the footprint of scalar fluxes; this ‘blocking effect’ is believed to lead to a smaller sensible heat flux above the canopy layer. Such an effect should decrease with height in the RSL, explaining the increase of the observed turbulent heat flux with height. In addition, the presence of non-uniform building heights adversely affects turbulence organization around the canopy top, and likely elevates the inflection point of the mean flow to a higher elevation close to the upper measurement level, where larger shear results in a larger momentum flux. For sector B, wake effects from the nearby high-rise buildings strongly reduce turbulence organization at higher elevations, leading to similar sensible heat and momentum fluxes at both measurement levels.

In this paper, a bi-level optimization model including the problem of transmission network market and energy management in the distribution substation is presented. In the proposed bi-level model, the lower level includes the demand-side management (DSM) program and the optimal charge/discharge of large-scale energy storage system (LSESS) at distribution substations to increase grid profits and send decisions to the upper-level transmission market operator. The upper level of the proposed model is a security-constrained unit commitment (SCUC) to minimize production, no-load, startup, shutdown, and active power curtailment costs, and also the unavailability of the generation units. In this paper, to solve the bi-level optimization problem, the Karush–Kuhn–Tucker (KKT) equation modeling method will be used to turn the problem into a single-level problem. One of the advantages of converting a bi-level model to a single-level model compared to the methods of the decomposition algorithms is the lack of use of iterative algorithms, which leads to an increase in problem-solving time. The proposed model is tested on standard distribution substations and transmission networks, which shows that the proposed method is more effective than decomposition algorithms in terms of problem-solving time. The simulation results showed that the proposed method can be more efficient in large optimization problems.

Data envelopment analysis for bi-level systems with multiple followers

Nowadays, power production, reliability, quality, efficiency and penetration of renewable energy sources are amongst the most important topics in the power systems analysis. The need to obtain optimal power management and economical dispatch are expressed at the same time. The interest in extracting an optimum performance minimizing market clearing price (MCP) for the consumers and provide better utilization of renewable energy sources has been increasing in recent years. Due to necessity of providing energy balance while having the fluctuations in the load demand and non-dispatchable nature of renewable sources, implementing an energy management system (EMS) is of great importance in Microgrids (MG). The appearance of new technologies such as energy storage (ES) has caused increase in the effort to present new and modified optimization methods for power management. Precise prediction of renewable energy sources power generation can only be provided with small anticipation. Hence, for increasing the efficiency of the presented optimization algorithm in large-dimension problems, new methods should be proposed, especially for short-term scheduling. Powerful optimization methods are needed to be applied in such a way to achieve maximum efficiency, enhance the economic dispatch as well as provide the best performance for these systems. Thus, real-time energy management within MG is an important factor for the operators to guarantee optimal and safe operation of the system. The proposed EMS should be able to schedule the MG generation with minimum information shares sent by generation units. To achieve this ability, the present thesis proposes an operational architecture for real time operation (RTO) of a MG operating in both islanding and grid-connected modes. The presented architecture is flexible and could be used for different configurations of MGs in different scenarios. A general formula is also presented to estimate optimum operation strategy, cost optimization plan and the reduction of the consumed electricity combined with applying demand response (DR). The proposed problem is formulated as an optimization problem with nonlinear constraints to minimize the cost related to generation sources and responsive load as well as reducing MCP. Several optimization methods including mixed linear programming, pivot source, imperialist competition, artificial bee colony, particle swarm, ant colony, and gravitational search algorithms are utilized to achieve the specified objectives. The main goal of the thesis is to validate experimentally the design of the real-time energy management system for MGs in both operating modes which is suitable for different size and types of generation resources and storage devices with plug-and-play structure. As a result, this system is capable of adapting itself to changes in the generation and storage assets in real-time, and delivering optimal operation commands to the assets quickly, using a local energy market (LEM) structure based on single side or double side auction. The study is aimed to figure the optimum operation of micro-sources out as well as to decrease the electricity production cost by hourly day-ahead and real time scheduling. Experimental results show the effectiveness of the proposed methods for optimal operation with minimum cost and plug-and-play capability in a MG. Moreover, these algorithms are feasible from computational viewpoints while having many advantages such as reducing the peak consumption, optimal operation and scheduling the generation unit as well as minimizing the electricity generation cost. Furthermore, capabilities such as the system development, reliability and flexibility are also considered in the proposed algorithms. The plug and play capability in real time applications is investigated by using different scenarios.