Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

Modeling and Design of Real-Time Pricing Systems Based on Markov Decision Processes

A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Design of Real-time Electricity Prices and Wireless Communication Smart Meter

Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.

A Research of Real-Time Pricing Mechanism and Its Characteristics

Real-Time Pricing (RTP) is proposed as an effective Demand-Side Management (DSM) to adjust the load curve in order to achieve the peak load shifting. At the same time, the RTP mechanism can also raise the revenue of the supply-side and reduce the electricity expenses of consumers to achieve a win-win situation. In this paper, a real-time pricing algorithm based on price elasticity theory is proposed to analyze the energy consumption and the response of the consumers in smart grid structure. We consider a smart grid equipped with smart meters and two-way communication system. By using real data to simulate the proposed model, some characteristics of RTP are summarized as follows: 1) Under the condition of the real data, the adjustment of load curve and reducing the expenses of consumers is obviously. But the profit of power supplier is difficult to ensure. If we balance the profits of both sides, the supplier and consumers, the profits of both sides and the adjustment of load curve will be relatively limited. 2) If assuming the response degree of consumers to real-time prices is high enough, the RTP mechanism can achieve the expected effect. 3, If the cost of supply-side (day-ahead price) fluctuates dramatically, the profits of both sides can be ensured to achieve the expected effect.

Verification of Real-Time Pricing Systems Based on Probabilistic Boolean Networks

In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.

一种MPEG-4 over RTP的实现方法

提出一种使用RTP（Real-Time Transport Protocol）来传输MPEG-4流的方法。RTP的几个优越的特性能保证MPEG-4流在IP网上的实时传输。另外，使用RTP还能使MPEG-4流与其它的实时数据流保证同步。

RTP payload format for H.264/SVC scalable video coding

The scalable extension of H.264/AVC, known as scalable video coding or SVC, is currently the main focus of the Joint Video Team’s work. In its present working draft, the higher level syntax of SVC follows the design principles of H.264/AVC. Self-contained network abstraction layer units (NAL units) form natural entities for packetization. The SVC specification is by no means finalized yet, but nevertheless the work towards an optimized RTP payload format has already started. RFC 3984, the RTP payload specification for H.264/AVC has been taken as a starting point, but it became quickly clear that the scalable features of SVC require adaptation in at least the areas of capability/operation point signaling and documentation of the extended NAL unit header. This paper first gives an overview of the history of scalable video coding, and then reviews the video coding layer (VCL) and NAL of the latest SVC draft specification. Finally, it discusses different aspects of the draft SVC RTP payload format, in- cluding the design criteria, use cases, signaling and payload structure.

Real-time pricing method for smart grids based on complementarity problem

Considering a demand response(DR)based social welfare maximization model,a complementarity problem based on the Karush-Kuhn-Tuker condition is described,which is a non-dual method for determining real-time price for smart grids.The Lagrange multiplier in the dual method,which is used to determine the basic electricity price,is applied in the model.The proposed method computes the optimal electricity consumption,price and production.According to the electricity price,users can arrange their electricity equipment reasonably to reduce the consumption pressure at peak time.The model aims to encourage users to actively participate in the DR and realize peak cutting and valley filling.In addition,the model considers different utility functions representing three types of users.Finally,a Jacobian smoothing version of Newton method is used to solve the model.Statistical simulations of the model validate the rationality and feasibility of the proposed method.

Personalized real-time pricing for efficient and fair demandresponse in energy cooperatives and highly competitive flexibility markets

This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency.Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness.In this paper, a personalized real-time pricing(P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it.Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare(user satisfaction)of electricity consumers.The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

A pricing scheme for electric utility’s participation in day-ahead and real-time flexibility energy markets

Liberalized electricity markets,smart grids and high penetration of renewable energy sources(RESs)led to the development of novel markets,whose objective is the harmonization between production and demand,usually noted as real time of flexibility markets.This necessitates the development of novel pricing schemes able to allow energy service providers(ESPs)to maximize their aggregated profits from the traditional markets(trading between wholesale/day-ahead and retail markets)and the innovative flexibility markets.In the same time,ESPs have to offer their end users(consumers)competitive(low cost)energy services.In this context,novel pricing schemes must act,among others,as automated demand side management(DSM)techniques that are able to trigger the desired behavioral changes according to the flexibility market prices in energy consumption curves(ECCs)of the consumers.Energy pricing schemes proposed so far,e.g.realtime pricing,interact in an efficient way with wholesale market.But they do not provide consumers with strong enough financial incentives to modify their energy consumption habits towards energy cost curtailment.Thus,they do not interact efficiently with flexibility markets.Therefore,we develop a flexibility real-time pricing(FRTP)scheme,which offers a dynamically adjustable level of financial incentives to participating users by fairly rewarding the ones that make desirable behavioral changes in their ECCs.Performance evaluation results demonstrate that the proposed FRTP is able to offer a 15%–30%more attractive trade-off between the stacked profits of ESPs,i.e.the sum of the profits from retail and flexibility markets,and the satisfaction of consumers.

Economic optimization of smart distribution networks considering real-time pricing

With the development of smart meters,a realtime pricing(RTP)demand response is becoming possible for households in distribution networks.The power flow can be bidirectional in distribution networks which become smarter with distributed generators(DGs).It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use,so that it is more economical to use electricity generated by local distributed generators.Therefore,in order to curtail operating costs of distribution networks,this paper proposes a model of economic optimization conducted by distribution network operators.The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators.Further,based on price elasticity,the formulations of load demand considering RTP are proposed with economic optimization of distribution networks.The economic optimization problems are resolved by an interior point method.The case study shows that peak load demand can be reduced about 3.5%because the household RTP and electricity purchasing costs of distribution network operators can save 28.86£every hour.

Real-Time Pricing of Smart Grid Based on Piece-Wise Linear Functions

In a power grid system, utility is a measure of the satisfaction of users’ electricity consumption;cost is a monetary value of electricity generated by the supplier. The utility and cost functions represent the satisfaction of different users and the supplier. Quadratic utility, logarithmic utility,and quadratic cost functions are widely used in social welfare maximization models of real-time pricing. These functions are not universal;they have to be discussed in detail for individual models. To overcome this problem, a piece-wise linear utility function and a piece-wise linear cost function with general properties are proposed in this paper. By smoothing the piece-wise linear utility and cost functions, a social welfare maximization model can be transformed into a differentiable convex optimization problem. A dual optimization method is used to solve the smoothed model. Through mathematical deduction and numerical simulations, the rationality of the model and the validity of the algorithm are verified as long as the elastic and cost coefficients take appropriate values. Thus, different user types and the supplier can be determined by selecting different elastic and cost coefficients.

Real-Time Pricing for Smart Grid with Multiple Companies and Multiple Users Using Two-Stage Optimization

In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies’ profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users’ daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm(C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company’s privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.

Planning and Real-time Pricing of EV Charging Stations Considering Social Welfare and Profitability Balance

This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.

DISRUPTION MANAGEMENT FOR SUPPLY CHAIN COORDINATION WITH EXPONENTIAL DEMAND FUNCTION

The coordination problem of a supply chain comprising one supplier and one retailer under market demand disruption is studied in this article. A novel exponential demand function is adopted, and the penalty cost is introduced explicitly to capture the deviation production cost caused by the market demand disruption. The optimal strategies are obtained for different disruption scale under the centralized mode. For the decentralized mode, it is proved that the supply chain can be fully coordinated by adjusting the price discount policy appropriately when disruption occurs. Furthermore, the authors point out that similar results can be established for more general demand functions that represent different market circumstances if certain assumptions are satisfied.

The impacts of market reform on the market penetration of natural gas-fired electricity and renewable energy in China

Natural gas-fired electricity(NGFE) is expected to play a more important role in the future due to its characteristics of low pollution, high efficiency and flexibility. However, its development in China is impeded by its high regulation price compared with coal power. Market reform is therefore of vital importance to promote the penetration of NGFE. The objective of this study is to analyze the impacts of market reform and the renewable electricity(RE) subsidy policy on the promotion of NGFE and RE. A dynamic game-theoretic model is developed to analyze the interaction among the NG supplier, the power sector and the power grid. Three scenarios are proposed with different policies, including a fixed regulation price of NG and electricity, real-time pricing(RTP) of NG and electricity, and subsidy targeted at RE. The results show that:(1) market reform can sharply decrease the NG price and consequently promote the development of NGFE and RE;(2) subsidy targeted at RE not only promotes the penetration of NGFE and RE, but also increases the utilization ratio of renewables significantly;(3) market reform and the subsidy also enhance consumers’ welfare by reducing their power consumption expenditure.

HEMS-enabled transactive flexibility in real-time operation of three-phase unbalanced distribution systems

This paper proposes a coordinated two-stage real-time market mechanism in an unbalanced distribution system which can utilize flexibility service from home energy management system(HEMS)to alleviate line congestion,voltage violation,and substation-level power imbalance.At the grid level,the distribution system operator(DSO)computes the distribution locational marginal prices(DLMPs)and its energy,loss,congestion,and voltage violation components through comprehensive sensitivity analyses.By using the DLMP components in a firststage optimization problem,the DSO generates two price signals and sends them to HEMS to seek flexibility service.In response to the request of DSO,each home-level HEMS computes a flexibility range by incorporating the prices of DSO in its own optimization problem.Due to future uncertainties,the HEMS optimization problem is modeled as an adaptive dynamic programming(ADP)to minimize the total expected cost and discomfort of the household over a forward-looking horizon.The flexibility range of each HEMS is then used by the DSO in a second-stage optimization problem to determine new optimal dispatch points which ensure the efficient,reliable,and congestionfree operation of the distribution system.Lastly,the second-stage dispatch points are used by each HEMS to constrain its maximum consumption level in a final ADP to assign consumption level of major appliances such as energy storage,heating,ventilation and air-conditioning,and water heater.The proposed method is validated on an IEEE 69-bus system with a large number of regular and HEMS-equipped homes in each phase.

Modelling and Verification of Real-Time Publish and Subscribe Protocol Using UPPAAL and Simulink/Stateflow

Real-Time Publish and Subscribe (RTPS) protocol is a protocol for implementing message exchange over an unreliable transport in data distribution service (DDS). Formal modelling and verification of the protocol provide stronger guarantees of its correctness and efficiency than testing alone. In this paper, we build formal models for the RTPS protocol using UPPAAL and Simulink/Stateflow. Modelling using Simulink/Stateflow allows analyzing the protocol through simula-tion, as well as generate executable code. Modelling using UPPAAL allows us to verify properties of the model stated in TCTL (Timed Computation Tree Logic), as well as estimate its performance using statistical model checking. We further describe a procedure for translation from Stateflow to timed automata, where a subset of major features in Stateflow is supported, and prove the soundness statement that the Stateflow model is a refinement of the translated timed automata model. As a consequence, any property in a certain fragment of TCTL that we have verified for the timed automata model in UPPAAL is preserved for the original Stateflow model.

Implementation of Mobile Streaming Media Player Based on BREW

Nowadays mobile streaming service through cell phone is becoming the highlight of new value-added mobile services. Based on the present CDMAlx wireless data network and Binary Runtime Environment for Wireless （BREW） platform, adopting compression technologies of H.264 and QCP, a set of streaming media players are designed and implemented, and the principle, structure, key technologies and performance analysis of this system are introduced. This player works well in practice.