Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

  • Volume 5,Issue 1,2017 Table of Contents
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    • >Special Section on Managing Electricity Demand
    • Guest Editorial: Special Section on Managing Electricity Demand

      2017, 5(1).

      Abstract (799) HTML (0) PDF 62.14 K (521) Comment (0) Favorites

      Abstract:The interest in managing electricity demand surfaced in earnest during the 1970s as economic, political, social, technological, and resource supply factors combined to change the electricity sectors’ operating environment and its outlook for the future. Ever since then, a successive series of concepts have evolved as an effective way of mitigating these risks including: demand-side management (DSM), demand response (DR), and transactive energy. Ten papers were accepted for this special section on Managing Electricity Demand.

    • Evolving practice of demand-side management

      2017, 5(1):1-9. DOI: 10.1007/s40565-016-0252-1

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      Abstract:The concept of demand-side management (DSM) was invented in the late 1970s along with the development of many of the frameworks in use to plan and implement it in the years immediately following. It was originally referred to as demand-side load management. It is generally defined as the planning and implementation of those activities designed to influence consumer use of electricity in ways that will result in changes in the utility’s load shape—i.e., changes in the time pattern and magnitude of a utility’s load. This paper describes the evolution it has undergone since its invention and some likely changes ahead. DSM largely originated in the U.S., but is practiced in various forms through the world today. This paper uses U.S. data as examples

    • From demand response to transactive energy: state of the art

      2017, 5(1):10-19. DOI: 10.1007/s40565-016-0256-x

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      Abstract:This paper reviews the state of the art of research and industry practice on demand response and the new methodology of transactive energy. Demand response programs incentivize consumers to align their demand with power supply conditions, enhancing power system reliability and economic operation. The design of demand response programs, performance of pilot projects and programs, consumer behaviors, and barriers are discussed. Transactive energy is a variant and a generalized form of demand response in that it manages both the supply and demand sides. It is intended for a changing environment with an increasing number of distributed resources and intelligent devices. It utilizes the flexibility of various generation/load resources to maintain a dynamic balance of supply and demand. These distributed resources are controlled by their owners. However, the design of transaction mechanisms should align the individual behaviors with the interests of the entire system. Transactive energy features real-time, autonomous, and decentralized decision making. The transition from demand response to transactive energy is also discussed.

    • Demand response for frequency control of multi-area power system

      2017, 5(1):20-29. DOI: 10.1007/s40565-016-0260-1

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      Abstract:Over the last few years, lots of attentions have been given to the demand response (DR) for the frequency control. DR can be incorporated with traditional frequency control method and enhance the stability of the system. In this paper, the frequency control strategy of DR for a multiarea power system is specially designed. In order to quickly stabilize the frequency of different areas, the tie-line power is adopted as the additional input signal of DR. To get the optimal parameters of the control system, the frequency control problem is formulated as a multi-objective optimization problem, and the parameters such as the integral gains of secondary frequency control, the frequency bias parameters, and coefficients ofDRare optimized. Numerical results verify the effectiveness of the proposed method.

    • Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid

      2017, 5(1):30-42. DOI: 10.1007/s40565-016-0255-y

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      Abstract:Thermostatically controlled appliances (TCAs) have great thermal storage capability and are therefore excellent demand response (DR) resources to solve the problem of power fluctuation caused by renewable energy. Traditional centralized management is affected by communication quality severely and thus usually has poor realtime control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation (OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.

    • Controllability and stability of primary frequency control from thermostatic loads with delays

      2017, 5(1):43-54. DOI: 10.1007/s40565-016-0262-z

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      Abstract:There is an increasing interest in exploiting the flexibility of loads to provide ancillary services to the grid. In this paper we study how response delays and lockout constraints affect the controllability of an aggregation of refrigerators offering primary frequency control (PFC). First we examine the effect of delays in PFC provision from an aggregation of refrigerators, using a two-area power system. We propose a framework to systematically address frequency measurement and response delays and we determine safe values for the total delays via simulations. We introduce a controllability index to evaluate PFC provision under lockout constraints of refrigerators compressors. We conduct extensive simulations to study the effects of measurement delay, ramping times, lockout durations and rotational inertia on the controllability of the aggregation and system stability. Finally, we discuss solutions for offering reliable PFC provision from thermostatically controlled loads under lockout constraints and we propose a supervisory control to enhance the robustness of their controllers.

    • Optimal air-conditioning load control in distribution network with intermittent renewables

      2017, 5(1):55-65. DOI: 10.1007/s40565-016-0254-z

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      Abstract:The coordinated operation of controllable loads, such as air-conditioning load, and distributed generation sources in a smart grid environment has drawn significant attention in recent years. To improve the wind power utilization level in the distribution network and minimize the total system operation costs, this paper proposes a MILP (mixed integer linear programming) based approach to schedule the interruptible air-conditioning loads. In order to mitigate the uncertainties of the stochastic variables including wind power generation, ambient temperature change, and electricity retail price, the rolling horizon optimization (RHO) strategy is employed to continuously update the real-time information and proceed the control window. Moreover, to ensure the thermal comfort of customers, a novel two-parameter thermal model is introduced to calculate the indoor temperature variation more precisely. Simulations on a five node radial distribution network validate the efficiency of the proposed method.

    • Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

      2017, 5(1):66-78. DOI: 10.1007/s40565-016-0257-9

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      Abstract:In recent years, much attention has been devoted to the development and applications of smart grid technologies, with special emphasis on flexible resources such as distributed generations (DGs), energy storages, active loads, and electric vehicles (EVs). Demand response (DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations. Despite their potential contributions to power system secure and economic operation, uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned. In addition, the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments. In this context, this paper aims to propose a DR based congestion management strategy for smart distribution systems. The general framework and procedures for distribution congestion management is first presented. A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established. Subsequently, the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices. The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.

    • Nodal user’s demand response based on incentive based programs

      2017, 5(1):79-90. DOI: 10.1007/s40565-016-0261-0

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      Abstract:This paper describes a practical approach to identify nodal price compensation payment for nodal consumers willing to reduce their energy consumption (consumers’ demand response). The implementation of a nodal reliability service pricing is based on contingency assessment of N - 2 order for transmission lines. A representative annualized demand curve is used to reflect the system’s operation condition by seasons. Such curve is used to access the nodal reliability impact trough a whole year in order to determine back-payments (incentive payment) to users for service interruption. The IEEE_RTS 24 nodes system is used to implement the proposed approach.

    • Residential end-uses disaggregation and demand response evaluation using integral transforms

      2017, 5(1):91-104. DOI: 10.1007/s40565-016-0258-8

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      Abstract:Demand response is a basic tool used to develop modern power systems and electricity markets. Residential and commercial segments account for 40%–50% of the overall electricity demand. These segments need to overcome major obstacles before they can be included in a demand response portfolio. The objective of this paper is to tackle some of the technical barriers and explain how the potential of enabling technology (smart meters) can be harnessed, to evaluate the potential of customers for demand response (end-uses and their behaviors) and, moreover, to validate customers’ effective response to market prices or system events by means of non-intrusive methods. A tool based on the Hilbert transform is improved herein to identify and characterize the most suitable loads for the aforesaid purpose, whereby important characteristics such as cycling frequency, power level and pulse width are identified. The proposed methodology allows the filtering of aggregated load according to the amplitudes of elemental loads, independently of the frequency of their behaviors that could be altered by internal or external inputs such as weather or demand response. In this way, the assessment and verification of customer response can be improved by solving the problem of load aggregation with the help of integral transforms.

    • Green neighbourhoods in low voltage networks: measuring impact of electric vehicles and photovoltaics on load profiles

      2017, 5(1):105-116. DOI: 10.1007/s40565-016-0253-0

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      Abstract:In the near future, various types of low-carbon technologies (LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage (LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods, where social influence is imposed. Real-life data from an LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood. This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.

    • >Regular Paper
    • Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

      2017, 5(1):117-125. DOI: 10.1007/s40565-016-0191-x

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      Abstract:To address the planning issue of offshore oilfield power systems, an integrated generation-transmission expansion planning model is proposed. The outage cost is considered and the genetic Tabu hybrid algorithm (GTHA) is developed to find the optimal solution. With the proposed integrated model, the planning of generators and transmission lines can be worked out simultaneously, which outweighs the disadvantages of separate planning, for instance, unable to consider the influence of power grid during the planning of generation, or insufficient to plan the transmission system without enough information of generation. The integrated planning model takes into account both the outage cost and the shipping cost, which makes the model more practical for offshore oilfield power systems. The planning problem formulated based on the proposed model is a mixed integer nonlinear programming problem of very high computational complexity, which is difficult to solve by regular mathematical methods. A comprehensive optimization method based on GTHA is also developed to search the best solution efficiently. Finally, a case study on the planning of a 50-bus offshore oilfield power system is conducted, and the obtained results fully demonstrate the effectiveness of the presented model and method.

    • Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

      2017, 5(1):126-133. DOI: 10.1007/s40565-015-0171-6

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      Abstract:Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending- intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model; `calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.

    • Universal generating function based probabilistic production simulation for wind power integrated power systems

      2017, 5(1):134-141. DOI: 10.1007/s40565-015-0114-2

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      Abstract:According to the demand of sustainable development and low-carbon electricity, it is important to develop clean resources and optimize scheduling generation mix. Firstly, a novel method for probabilistic production simulation for wind power integrated power systems is proposed based on universal generating function (UGF), which completes the production simulation with the chronological wind power and load demand. Secondly, multiple-period multiple-state wind power model and multiple-state thermal unit power model are adopted, and both thermal power and wind power are coordinately scheduled by the comprehensive cost including economic cost and environmental cost. Furthermore, the accommodation and curtailment of wind power is synergistically considered according to the available regulation capability of conventional generators in operation. Finally, the proposed method is verified and compared with conventional convolution method in the improved IEEE-RTS 79 system.

    • Directed graph-based distribution network reconfiguration for operation mode adjustment and service restoration considering distributed generation

      2017, 5(1):142-149. DOI: 10.1007/s40565-016-0198-3

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      Abstract:We present a directed graph-based method for distribution network reconfiguration considering distributed generation. Two reconfiguration situations are considered: operation mode adjustment with the objective of minimizing active power loss (situation I) and service restoration with the objective of maximizing loads restored (situation II). These two situations are modeled as a mixed integer quadratic programming problem and a mixed integer linear programming problem, respectively. The properties of the distribution network with distributed generation considered are reflected as the structure model and the constraints described by directed graph. More specifically, the concepts of ‘‘in-degree’’ and ‘‘out-degree’’ are presented to ensure the radial structure of the distribution network, and the concepts of ‘‘virtual node’’ and ‘‘virtual demand’’ are developed to ensure the connectivity of charged nodes in every independent power supply area. The validity and effectiveness of the proposed method are verified by test results of an IEEE 33-bus system and a 5-feeder system.