Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage

Zakernezhad, Hamid; Nazar, Mehrdad Setayesh; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.apenergy.2022.118865

Cited by 19 publications

(6 citation statements)

References 31 publications

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“… The relaxation of branch power flowThe non‐convex and non‐linear Equation (2) can be rewritten as a second‐order cone relax form, as presented in the following [38]

\begin{equation} {\left\Vert \def\eqcellsep{&}\begin{array}{l}{\left[2 P_{m n}^t \ \ 2 Q_{m n}^t \ \ i_{m n}^t-v_{m}^t \right]}^{\mathrm{T}} \end{array} \right\Vert} _{2} \leqslant i_{m n}^t+v_{m}^t \end{equation}

The capacities of branchesThe power flowing from node m to node n is limited to [10]

\begin{equation} {\left\lbrace \begin{aligned} P_{mn}^{\min } \le P_{mn}^t \le P_{mn}^{\max } \\ Q_{mn}^{\min } \le Q_{mn}^t \le Q_{mn}^{\max } \end{aligned} \right.} \end{equation}

Operation security and network loss

{v}_m^{t}

(

m \in {\mathcal {N}}_{\mathrm{n}ode}

) and

{i}_{mn}^{t}

(

mn \in {\mathcal {N}}_{\mathrm{b}ranch}

) should satisfy [39] …”

Section: Optimal Allocation Framework Of Photovoltaic Generations In ...mentioning

confidence: 99%

“…

{v}_m^{t}

(

m \in {\mathcal {N}}_{\mathrm{n}ode}

) and

{i}_{mn}^{t}

(

mn \in {\mathcal {N}}_{\mathrm{b}ranch}

) should satisfy [39]

\begin{equation} {v}^{\rm {min}}-\Delta v^{\mathrm{m}in} \le {v}_m^{t} \le {v}^{\rm {max}} +\Delta v^{\mathrm{m}ax} \end{equation}

\begin{equation} 0 &lt; {i}_{mn}^{t} \le {i}^{\rm {max}} \end{equation}

…”

Section: Optimal Allocation Framework Of Photovoltaic Generations In ...mentioning

confidence: 99%

“…3) Operation security and network loss v t m (m ∈  node ) and i t mn (mn ∈  branch ) should satisfy [39]…”

Section: Constraints Of a Distribution Network 1) The Relaxation Of B...mentioning

confidence: 99%

See 2 more Smart Citations

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Tang,

Huang,

Zhang

et al. 2023

IET Generation Trans & Dist

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A novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

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“… The relaxation of branch power flowThe non‐convex and non‐linear Equation (2) can be rewritten as a second‐order cone relax form, as presented in the following [38]

\begin{equation} {\left\Vert \def\eqcellsep{&}\begin{array}{l}{\left[2 P_{m n}^t \ \ 2 Q_{m n}^t \ \ i_{m n}^t-v_{m}^t \right]}^{\mathrm{T}} \end{array} \right\Vert} _{2} \leqslant i_{m n}^t+v_{m}^t \end{equation}

The capacities of branchesThe power flowing from node m to node n is limited to [10]

\begin{equation} {\left\lbrace \begin{aligned} P_{mn}^{\min } \le P_{mn}^t \le P_{mn}^{\max } \\ Q_{mn}^{\min } \le Q_{mn}^t \le Q_{mn}^{\max } \end{aligned} \right.} \end{equation}

Operation security and network loss

{v}_m^{t}

(

m \in {\mathcal {N}}_{\mathrm{n}ode}

) and

{i}_{mn}^{t}

(

mn \in {\mathcal {N}}_{\mathrm{b}ranch}

) should satisfy [39] …”

Section: Optimal Allocation Framework Of Photovoltaic Generations In ...mentioning

confidence: 99%

“…

{v}_m^{t}

(

m \in {\mathcal {N}}_{\mathrm{n}ode}

) and

{i}_{mn}^{t}

(

mn \in {\mathcal {N}}_{\mathrm{b}ranch}

) should satisfy [39]

\begin{equation} {v}^{\rm {min}}-\Delta v^{\mathrm{m}in} \le {v}_m^{t} \le {v}^{\rm {max}} +\Delta v^{\mathrm{m}ax} \end{equation}

\begin{equation} 0 &lt; {i}_{mn}^{t} \le {i}^{\rm {max}} \end{equation}

…”

Section: Optimal Allocation Framework Of Photovoltaic Generations In ...mentioning

confidence: 99%

See 1 more Smart Citation

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Tang,

Huang,

Zhang

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In [11], a two-level optimization approach was presented for the distribution system, which contemplates the demand response, electrical energy storage, and energy resources. The first level determines optimal bidding strategies for electric vehicles and demand response aggregators.…”

Section: Introductionmentioning

confidence: 99%

“…Evaluate the exploitation and exploration phases of heap algorithm in the energy management phases are expressed using eqn. (11),…”

mentioning

confidence: 99%

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Zhiqiang Wang

2024

jes

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The power grid presents several obstacles for demand forecasting and resource scheduling, such as a substantial amount of data, a growing number of factors influencing the demand profile, uncertainty in the generation profile of distributed energy from renewable sources, and a shortage of historical data. Here, we provide a unique market-oriented energy storage method based on artificial intelligence (AI) that aims to optimize operational profit in the electricity market between consumers, energy storage, and grid service providers. The approach is divided into two parts; the first is the Residual Network-50 (GR-ResNet-50) algorithm, which is used to overcome future uncertainties related to load needs and power pricing. The second uses deep learning based on the Radial Heap Basis Algorithm (RHBA) to determine the most effective charging or discharging operation considering the grid peak states, load demand, and the state of the batteries. The performance of the proposed method in this study is compared with the conventional method in terms of 0.007% of low MAPE and 20 seconds of less execution time. The research shows that the proposed strategy significantly lowers on-peak power, improves operational profit, and improves effective performance.

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Integrated framework for modeling the interactions of plug-in hybrid electric vehicles aggregators, parking lots and distributed generation facilities in electricity markets

et al. 2023

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Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage

Cited by 19 publications

References 31 publications

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

Demand Forecasting and Resource Scheduling of Independent Energy Storage Market in Power Grid with Deep Learning

Integrated framework for modeling the interactions of plug-in hybrid electric vehicles aggregators, parking lots and distributed generation facilities in electricity markets

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